yolov6_implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d933964d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!git clone https://github.com/meituan/YOLOv6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30f8fb79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\yolov6_env\\yolov6_custom_dataset\\YOLOv6\n"
     ]
    }
   ],
   "source": [
    "cd YOLOv6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4054bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -r requirements.txt\n",
    "# AssertionError: Torch not compiled with CUDA enabled\n",
    "\n",
    "# This requirement file is not installing torch with cuda. So if you want to train your model using gpu then install torch with cuda\n",
    "\n",
    "# Below torch version works with rtx 3090 (cuda 11.1)\n",
    "#!pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2aa6501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NVIDIA GeForce RTX 3090'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available() \n",
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff00a081",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put your dataset folder in yolov6 folder\n",
    "# create dataset.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b897b5c2",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "002e0e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading annotations into memory...\n",
      "Done (t=0.01s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.24s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.97s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.53s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.034\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.052\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.040\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.040\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.287\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.572\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.588\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.191\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.22s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.92s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.50s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.065\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.089\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.079\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.013\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.072\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.352\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.633\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.652\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.18s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.92s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.45s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.081\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.108\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.095\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.043\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.087\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.414\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.687\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.697\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.439\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.721\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.13s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.89s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.43s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.121\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.160\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.138\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.037\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.130\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.437\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.687\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.708\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.736\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.12s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.85s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.40s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.139\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.184\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.160\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.058\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.147\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.429\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.702\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.714\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.733\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.17s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.80s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.40s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.128\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.175\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.154\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.064\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.135\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.444\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.691\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.707\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.459\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.725\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.12s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.84s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.40s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.154\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.207\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.176\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.160\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.454\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.715\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.729\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.750\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.11s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.79s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.37s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.185\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.247\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.211\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.123\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.188\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.460\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.727\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.736\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.477\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.754\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.14s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.78s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.39s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.214\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.279\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.241\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.084\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.222\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.462\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.728\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.737\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.753\n",
      "loading annotations into memory...\n",
      "Done (t=0.00s)\n",
      "creating index...\n",
      "index created!\n",
      "Loading and preparing results...\n",
      "DONE (t=0.11s)\n",
      "creating index...\n",
      "index created!\n",
      "Running per image evaluation...\n",
      "Evaluate annotation type *bbox*\n",
      "DONE (t=0.83s).\n",
      "Accumulating evaluation results...\n",
      "DONE (t=0.37s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.212\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.280\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.241\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.096\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.224\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.463\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.731\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.742\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "training args are: Namespace(batch_size=16, check_images=False, check_labels=False, conf_file='configs/yolov6s_finetune.py', data_path='dataset.yaml', device='0', dist_url='default url: tcp://127.0.0.1:8888', epochs=10, eval_final_only=False, eval_interval=2, gpu_count=0, heavy_eval_range=50, img_size=640, local_rank=-1, name='exp', output_dir='./runs/train', rank=-1, resume=None, workers=8, world_size=1)\n",
      "\n",
      "Using 1 GPU for training... \n",
      "Train: Final numbers of valid images: 1529/ labels: 1529. \n",
      "0.0s for dataset initialization.\n",
      "Convert to COCO format\n",
      "\n",
      "  0%|          | 0/239 [00:00<?, ?it/s]\n",
      "100%|##########| 239/239 [00:00<00:00, 47798.91it/s]\n",
      "Convert to COCO format finished. Resutls saved in ../data\\annotations\\instances_val.json\n",
      "Val: Final numbers of valid images: 239/ labels: 239. \n",
      "0.0s for dataset initialization.\n",
      "Loading state_dict from ./weights/yolov6s.pt for fine-tuning...\n",
      "Model: Model(\n",
      "  (backbone): EfficientRep(\n",
      "    (stem): RepVGGBlock(\n",
      "      (nonlinearity): ReLU(inplace=True)\n",
      "      (se): Identity()\n",
      "      (rbr_dense): Sequential(\n",
      "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      )\n",
      "      (rbr_1x1): Sequential(\n",
      "        (conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (ERBlock_2): Sequential(\n",
      "      (0): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): RepBlock(\n",
      "        (conv1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (block): Sequential(\n",
      "          (0): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ERBlock_3): Sequential(\n",
      "      (0): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): RepBlock(\n",
      "        (conv1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (block): Sequential(\n",
      "          (0): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (2): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ERBlock_4): Sequential(\n",
      "      (0): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): RepBlock(\n",
      "        (conv1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (block): Sequential(\n",
      "          (0): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (1): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (2): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (3): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "          (4): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ERBlock_5): Sequential(\n",
      "      (0): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (1): RepBlock(\n",
      "        (conv1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (block): Sequential(\n",
      "          (0): RepVGGBlock(\n",
      "            (nonlinearity): ReLU(inplace=True)\n",
      "            (se): Identity()\n",
      "            (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            (rbr_dense): Sequential(\n",
      "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "            (rbr_1x1): Sequential(\n",
      "              (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "            )\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "      (2): SimSPPF(\n",
      "        (cv1): SimConv(\n",
      "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (act): ReLU(inplace=True)\n",
      "        )\n",
      "        (cv2): SimConv(\n",
      "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (act): ReLU(inplace=True)\n",
      "        )\n",
      "        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (neck): RepPANNeck(\n",
      "    (Rep_p4): RepBlock(\n",
      "      (conv1): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (block): Sequential(\n",
      "        (0): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (2): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (Rep_p3): RepBlock(\n",
      "      (conv1): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (block): Sequential(\n",
      "        (0): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (2): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (Rep_n3): RepBlock(\n",
      "      (conv1): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (block): Sequential(\n",
      "        (0): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (2): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (Rep_n4): RepBlock(\n",
      "      (conv1): RepVGGBlock(\n",
      "        (nonlinearity): ReLU(inplace=True)\n",
      "        (se): Identity()\n",
      "        (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (rbr_dense): Sequential(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "        (rbr_1x1): Sequential(\n",
      "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        )\n",
      "      )\n",
      "      (block): Sequential(\n",
      "        (0): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (1): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "        (2): RepVGGBlock(\n",
      "          (nonlinearity): ReLU(inplace=True)\n",
      "          (se): Identity()\n",
      "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          (rbr_dense): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "          (rbr_1x1): Sequential(\n",
      "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (reduce_layer0): SimConv(\n",
      "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "    )\n",
      "    (upsample0): Transpose(\n",
      "      (upsample_transpose): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
      "    )\n",
      "    (reduce_layer1): SimConv(\n",
      "      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "    )\n",
      "    (upsample1): Transpose(\n",
      "      (upsample_transpose): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
      "    )\n",
      "    (downsample2): SimConv(\n",
      "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "    )\n",
      "    (downsample1): SimConv(\n",
      "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "      (act): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (detect): Detect(\n",
      "    (cls_convs): ModuleList(\n",
      "      (0): Conv(\n",
      "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (1): Conv(\n",
      "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (2): Conv(\n",
      "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (reg_convs): ModuleList(\n",
      "      (0): Conv(\n",
      "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (1): Conv(\n",
      "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (2): Conv(\n",
      "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "    (cls_preds): ModuleList(\n",
      "      (0): Conv2d(64, 30, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (1): Conv2d(128, 30, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (2): Conv2d(256, 30, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (reg_preds): ModuleList(\n",
      "      (0): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (2): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (obj_preds): ModuleList(\n",
      "      (0): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (2): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
      "    )\n",
      "    (stems): ModuleList(\n",
      "      (0): Conv(\n",
      "        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (1): Conv(\n",
      "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "      (2): Conv(\n",
      "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
      "        (act): SiLU(inplace=True)\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n",
      "Training start...\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       0/9     2.214     1.312     5.643     4.328:   0%|          | 0/96 [00:01\n",
      "       0/9     2.214     1.312     5.643     4.328:   1%|1         | 1/96 [00:01\n",
      "       0/9     2.326     1.437     5.842      4.09:   1%|1         | 1/96 [00:01\n",
      "       0/9     2.326     1.437     5.842      4.09:   2%|2         | 2/96 [00:01\n",
      "       0/9      2.32     1.567     6.038     3.978:   2%|2         | 2/96 [00:01\n",
      "       0/9      2.32     1.567     6.038     3.978:   3%|3         | 3/96 [00:01\n",
      "       0/9     2.284     1.545     6.124     3.963:   3%|3         | 3/96 [00:01\n",
      "       0/9     2.284     1.545     6.124     3.963:   4%|4         | 4/96 [00:01\n",
      "       0/9     2.272      1.52     6.233     3.961:   4%|4         | 4/96 [00:02\n",
      "       0/9     2.272      1.52     6.233     3.961:   5%|5         | 5/96 [00:02\n",
      "       0/9     2.283     1.518     6.201     3.961:   5%|5         | 5/96 [00:02\n",
      "       0/9     2.283     1.518     6.201     3.961:   6%|6         | 6/96 [00:02\n",
      "       0/9     2.255     1.516     6.174      3.96:   6%|6         | 6/96 [00:02\n",
      "       0/9     2.255     1.516     6.174      3.96:   7%|7         | 7/96 [00:02\n",
      "       0/9     2.313     1.552     6.145     3.942:   7%|7         | 7/96 [00:03\n",
      "       0/9     2.313     1.552     6.145     3.942:   8%|8         | 8/96 [00:03\n",
      "       0/9     2.287     1.532     6.166     3.935:   8%|8         | 8/96 [00:03\n",
      "       0/9     2.287     1.532     6.166     3.935:   9%|9         | 9/96 [00:03\n",
      "       0/9      2.27     1.533     6.186     3.934:   9%|9         | 9/96 [00:03\n",
      "       0/9      2.27     1.533     6.186     3.934:  10%|#         | 10/96 [00:0\n",
      "       0/9     2.274     1.515     6.166     3.919:  10%|#         | 10/96 [00:0\n",
      "       0/9     2.274     1.515     6.166     3.919:  11%|#1        | 11/96 [00:0\n",
      "       0/9      2.28     1.521     6.139     3.895:  11%|#1        | 11/96 [00:0\n",
      "       0/9      2.28     1.521     6.139     3.895:  12%|#2        | 12/96 [00:0\n",
      "       0/9     2.261     1.505     6.151     3.915:  12%|#2        | 12/96 [00:0\n",
      "       0/9     2.261     1.505     6.151     3.915:  14%|#3        | 13/96 [00:0\n",
      "       0/9     2.263     1.498     6.106     3.903:  14%|#3        | 13/96 [00:0\n",
      "       0/9     2.263     1.498     6.106     3.903:  15%|#4        | 14/96 [00:0\n",
      "       0/9     2.258     1.502     6.046     3.902:  15%|#4        | 14/96 [00:0\n",
      "       0/9     2.258     1.502     6.046     3.902:  16%|#5        | 15/96 [00:0\n",
      "       0/9     2.263     1.498      6.01      3.89:  16%|#5        | 15/96 [00:0\n",
      "       0/9     2.263     1.498      6.01      3.89:  17%|#6        | 16/96 [00:0\n",
      "       0/9     2.235      1.47     5.951     3.909:  17%|#6        | 16/96 [00:0\n",
      "       0/9     2.235      1.47     5.951     3.909:  18%|#7        | 17/96 [00:0\n",
      "       0/9     2.233     1.468     5.938     3.915:  18%|#7        | 17/96 [00:0\n",
      "       0/9     2.233     1.468     5.938     3.915:  19%|#8        | 18/96 [00:0\n",
      "       0/9     2.224      1.47     5.955     3.914:  19%|#8        | 18/96 [00:0\n",
      "       0/9     2.224      1.47     5.955     3.914:  20%|#9        | 19/96 [00:0\n",
      "       0/9     2.223     1.467     5.923     3.902:  20%|#9        | 19/96 [00:0\n",
      "       0/9     2.223     1.467     5.923     3.902:  21%|##        | 20/96 [00:0\n",
      "       0/9     2.206      1.45     5.913     3.933:  21%|##        | 20/96 [00:0\n",
      "       0/9     2.206      1.45     5.913     3.933:  22%|##1       | 21/96 [00:0\n",
      "       0/9     2.198     1.447     5.923     3.924:  22%|##1       | 21/96 [00:0\n",
      "       0/9     2.198     1.447     5.923     3.924:  23%|##2       | 22/96 [00:0\n",
      "       0/9     2.182      1.44     5.906     3.928:  23%|##2       | 22/96 [00:0\n",
      "       0/9     2.182      1.44     5.906     3.928:  24%|##3       | 23/96 [00:0\n",
      "       0/9     2.169     1.432     5.871     3.935:  24%|##3       | 23/96 [00:0\n",
      "       0/9     2.169     1.432     5.871     3.935:  25%|##5       | 24/96 [00:0\n",
      "       0/9     2.174     1.434     5.871     3.933:  25%|##5       | 24/96 [00:0\n",
      "       0/9     2.174     1.434     5.871     3.933:  26%|##6       | 25/96 [00:0\n",
      "       0/9     2.187     1.444     5.897      3.92:  26%|##6       | 25/96 [00:0\n",
      "       0/9     2.187     1.444     5.897      3.92:  27%|##7       | 26/96 [00:0\n",
      "       0/9     2.189     1.432     5.882     3.919:  27%|##7       | 26/96 [00:0\n",
      "       0/9     2.189     1.432     5.882     3.919:  28%|##8       | 27/96 [00:0\n",
      "       0/9     2.181     1.427     5.853     3.918:  28%|##8       | 27/96 [00:0\n",
      "       0/9     2.181     1.427     5.853     3.918:  29%|##9       | 28/96 [00:0\n",
      "       0/9     2.181     1.425     5.831     3.914:  29%|##9       | 28/96 [00:0\n",
      "       0/9     2.181     1.425     5.831     3.914:  30%|###       | 29/96 [00:0\n",
      "       0/9      2.18     1.418     5.797     3.917:  30%|###       | 29/96 [00:0\n",
      "       0/9      2.18     1.418     5.797     3.917:  31%|###1      | 30/96 [00:0\n",
      "       0/9     2.183     1.419     5.782     3.915:  31%|###1      | 30/96 [00:0\n",
      "       0/9     2.183     1.419     5.782     3.915:  32%|###2      | 31/96 [00:0\n",
      "       0/9     2.177     1.418     5.771     3.907:  32%|###2      | 31/96 [00:0\n",
      "       0/9     2.177     1.418     5.771     3.907:  33%|###3      | 32/96 [00:0\n",
      "       0/9     2.169     1.414     5.751     3.899:  33%|###3      | 32/96 [00:0\n",
      "       0/9     2.169     1.414     5.751     3.899:  34%|###4      | 33/96 [00:0\n",
      "       0/9      2.17     1.416     5.725     3.901:  34%|###4      | 33/96 [00:0\n",
      "       0/9      2.17     1.416     5.725     3.901:  35%|###5      | 34/96 [00:0\n",
      "       0/9     2.161     1.408     5.697       3.9:  35%|###5      | 34/96 [00:0\n",
      "       0/9     2.161     1.408     5.697       3.9:  36%|###6      | 35/96 [00:0\n",
      "       0/9      2.16     1.403     5.679     3.893:  36%|###6      | 35/96 [00:0\n",
      "       0/9      2.16     1.403     5.679     3.893:  38%|###7      | 36/96 [00:0\n",
      "       0/9     2.159     1.406     5.664     3.884:  38%|###7      | 36/96 [00:1\n",
      "       0/9     2.159     1.406     5.664     3.884:  39%|###8      | 37/96 [00:1\n",
      "       0/9     2.158     1.404      5.66     3.879:  39%|###8      | 37/96 [00:1\n",
      "       0/9     2.158     1.404      5.66     3.879:  40%|###9      | 38/96 [00:1\n",
      "       0/9     2.163     1.398     5.635     3.877:  40%|###9      | 38/96 [00:1\n",
      "       0/9     2.163     1.398     5.635     3.877:  41%|####      | 39/96 [00:1\n",
      "       0/9     2.155     1.394      5.62     3.875:  41%|####      | 39/96 [00:1\n",
      "       0/9     2.155     1.394      5.62     3.875:  42%|####1     | 40/96 [00:1\n",
      "       0/9     2.154     1.393     5.603     3.875:  42%|####1     | 40/96 [00:1\n",
      "       0/9     2.154     1.393     5.603     3.875:  43%|####2     | 41/96 [00:1\n",
      "       0/9     2.149     1.387     5.574     3.886:  43%|####2     | 41/96 [00:1\n",
      "       0/9     2.149     1.387     5.574     3.886:  44%|####3     | 42/96 [00:1\n",
      "       0/9     2.144     1.389     5.567     3.886:  44%|####3     | 42/96 [00:1\n",
      "       0/9     2.144     1.389     5.567     3.886:  45%|####4     | 43/96 [00:1\n",
      "       0/9     2.144     1.392     5.555     3.887:  45%|####4     | 43/96 [00:1\n",
      "       0/9     2.144     1.392     5.555     3.887:  46%|####5     | 44/96 [00:1\n",
      "       0/9     2.133     1.386     5.531     3.888:  46%|####5     | 44/96 [00:1\n",
      "       0/9     2.133     1.386     5.531     3.888:  47%|####6     | 45/96 [00:1\n",
      "       0/9     2.124     1.382     5.511     3.884:  47%|####6     | 45/96 [00:1\n",
      "       0/9     2.124     1.382     5.511     3.884:  48%|####7     | 46/96 [00:1\n",
      "       0/9      2.12     1.382     5.496     3.881:  48%|####7     | 46/96 [00:1\n",
      "       0/9      2.12     1.382     5.496     3.881:  49%|####8     | 47/96 [00:1\n",
      "       0/9     2.115     1.378     5.482     3.889:  49%|####8     | 47/96 [00:1\n",
      "       0/9     2.115     1.378     5.482     3.889:  50%|#####     | 48/96 [00:1\n",
      "       0/9     2.111     1.372     5.465     3.893:  50%|#####     | 48/96 [00:1\n",
      "       0/9     2.111     1.372     5.465     3.893:  51%|#####1    | 49/96 [00:1\n",
      "       0/9     2.121     1.374     5.457     3.897:  51%|#####1    | 49/96 [00:1\n",
      "       0/9     2.121     1.374     5.457     3.897:  52%|#####2    | 50/96 [00:1\n",
      "       0/9      2.12     1.374     5.439     3.889:  52%|#####2    | 50/96 [00:1\n",
      "       0/9      2.12     1.374     5.439     3.889:  53%|#####3    | 51/96 [00:1\n",
      "       0/9      2.12     1.373     5.421     3.887:  53%|#####3    | 51/96 [00:1\n",
      "       0/9      2.12     1.373     5.421     3.887:  54%|#####4    | 52/96 [00:1\n",
      "       0/9     2.116     1.368     5.405     3.887:  54%|#####4    | 52/96 [00:1\n",
      "       0/9     2.116     1.368     5.405     3.887:  55%|#####5    | 53/96 [00:1\n",
      "       0/9     2.115     1.363     5.383     3.886:  55%|#####5    | 53/96 [00:1\n",
      "       0/9     2.115     1.363     5.383     3.886:  56%|#####6    | 54/96 [00:1\n",
      "       0/9     2.112     1.359     5.366     3.884:  56%|#####6    | 54/96 [00:1\n",
      "       0/9     2.112     1.359     5.366     3.884:  57%|#####7    | 55/96 [00:1\n",
      "       0/9     2.109     1.357     5.351     3.878:  57%|#####7    | 55/96 [00:1\n",
      "       0/9     2.109     1.357     5.351     3.878:  58%|#####8    | 56/96 [00:1\n",
      "       0/9     2.108     1.352     5.335     3.876:  58%|#####8    | 56/96 [00:1\n",
      "       0/9     2.108     1.352     5.335     3.876:  59%|#####9    | 57/96 [00:1\n",
      "       0/9     2.104     1.348     5.316     3.875:  59%|#####9    | 57/96 [00:1\n",
      "       0/9     2.104     1.348     5.316     3.875:  60%|######    | 58/96 [00:1\n",
      "       0/9     2.101     1.345     5.296     3.872:  60%|######    | 58/96 [00:1\n",
      "       0/9     2.101     1.345     5.296     3.872:  61%|######1   | 59/96 [00:1\n",
      "       0/9     2.102     1.345     5.282     3.863:  61%|######1   | 59/96 [00:1\n",
      "       0/9     2.102     1.345     5.282     3.863:  62%|######2   | 60/96 [00:1\n",
      "       0/9     2.101     1.346     5.269     3.856:  62%|######2   | 60/96 [00:1\n",
      "       0/9     2.101     1.346     5.269     3.856:  64%|######3   | 61/96 [00:1\n",
      "       0/9     2.106     1.348     5.256     3.849:  64%|######3   | 61/96 [00:1\n",
      "       0/9     2.106     1.348     5.256     3.849:  65%|######4   | 62/96 [00:1\n",
      "       0/9     2.104     1.345     5.241     3.843:  65%|######4   | 62/96 [00:1\n",
      "       0/9     2.104     1.345     5.241     3.843:  66%|######5   | 63/96 [00:1\n",
      "       0/9     2.105     1.343     5.225     3.839:  66%|######5   | 63/96 [00:1\n",
      "       0/9     2.105     1.343     5.225     3.839:  67%|######6   | 64/96 [00:1\n",
      "       0/9     2.103     1.339     5.208     3.838:  67%|######6   | 64/96 [00:1\n",
      "       0/9     2.103     1.339     5.208     3.838:  68%|######7   | 65/96 [00:1\n",
      "       0/9       2.1     1.337     5.192     3.837:  68%|######7   | 65/96 [00:1\n",
      "       0/9       2.1     1.337     5.192     3.837:  69%|######8   | 66/96 [00:1\n",
      "       0/9     2.096     1.334     5.179     3.836:  69%|######8   | 66/96 [00:1\n",
      "       0/9     2.096     1.334     5.179     3.836:  70%|######9   | 67/96 [00:1\n",
      "       0/9     2.089      1.33     5.164     3.837:  70%|######9   | 67/96 [00:1\n",
      "       0/9     2.089      1.33     5.164     3.837:  71%|#######   | 68/96 [00:1\n",
      "       0/9     2.085     1.326     5.148     3.835:  71%|#######   | 68/96 [00:1\n",
      "       0/9     2.085     1.326     5.148     3.835:  72%|#######1  | 69/96 [00:1\n",
      "       0/9     2.082     1.324     5.136     3.831:  72%|#######1  | 69/96 [00:1\n",
      "       0/9     2.082     1.324     5.136     3.831:  73%|#######2  | 70/96 [00:1\n",
      "       0/9     2.077     1.322     5.121     3.827:  73%|#######2  | 70/96 [00:1\n",
      "       0/9     2.077     1.322     5.121     3.827:  74%|#######3  | 71/96 [00:1\n",
      "       0/9     2.074     1.321     5.115      3.82:  74%|#######3  | 71/96 [00:1\n",
      "       0/9     2.074     1.321     5.115      3.82:  75%|#######5  | 72/96 [00:1\n",
      "       0/9     2.074     1.319     5.107     3.817:  75%|#######5  | 72/96 [00:1\n",
      "       0/9     2.074     1.319     5.107     3.817:  76%|#######6  | 73/96 [00:1\n",
      "       0/9     2.081     1.321     5.106     3.815:  76%|#######6  | 73/96 [00:1\n",
      "       0/9     2.081     1.321     5.106     3.815:  77%|#######7  | 74/96 [00:1\n",
      "       0/9     2.079     1.319     5.099     3.814:  77%|#######7  | 74/96 [00:1\n",
      "       0/9     2.079     1.319     5.099     3.814:  78%|#######8  | 75/96 [00:1\n",
      "       0/9     2.077     1.317     5.087     3.811:  78%|#######8  | 75/96 [00:1\n",
      "       0/9     2.077     1.317     5.087     3.811:  79%|#######9  | 76/96 [00:1\n",
      "       0/9     2.074     1.314     5.071     3.807:  79%|#######9  | 76/96 [00:2\n",
      "       0/9     2.074     1.314     5.071     3.807:  80%|########  | 77/96 [00:2\n",
      "       0/9     2.069      1.31      5.06     3.805:  80%|########  | 77/96 [00:2\n",
      "       0/9     2.069      1.31      5.06     3.805:  81%|########1 | 78/96 [00:2\n",
      "       0/9     2.065      1.31     5.049     3.803:  81%|########1 | 78/96 [00:2\n",
      "       0/9     2.065      1.31     5.049     3.803:  82%|########2 | 79/96 [00:2\n",
      "       0/9     2.064     1.309      5.04     3.798:  82%|########2 | 79/96 [00:2\n",
      "       0/9     2.064     1.309      5.04     3.798:  83%|########3 | 80/96 [00:2\n",
      "       0/9     2.063     1.308     5.026     3.792:  83%|########3 | 80/96 [00:2\n",
      "       0/9     2.063     1.308     5.026     3.792:  84%|########4 | 81/96 [00:2\n",
      "       0/9     2.061     1.306     5.019     3.788:  84%|########4 | 81/96 [00:2\n",
      "       0/9     2.061     1.306     5.019     3.788:  85%|########5 | 82/96 [00:2\n",
      "       0/9     2.057     1.305     5.004     3.785:  85%|########5 | 82/96 [00:2\n",
      "       0/9     2.057     1.305     5.004     3.785:  86%|########6 | 83/96 [00:2\n",
      "       0/9     2.055     1.302     4.995     3.782:  86%|########6 | 83/96 [00:2\n",
      "       0/9     2.055     1.302     4.995     3.782:  88%|########7 | 84/96 [00:2\n",
      "       0/9     2.051       1.3     4.983     3.778:  88%|########7 | 84/96 [00:2\n",
      "       0/9     2.051       1.3     4.983     3.778:  89%|########8 | 85/96 [00:2\n",
      "       0/9      2.05     1.298     4.976     3.777:  89%|########8 | 85/96 [00:2\n",
      "       0/9      2.05     1.298     4.976     3.777:  90%|########9 | 86/96 [00:2\n",
      "       0/9     2.048     1.296     4.968     3.774:  90%|########9 | 86/96 [00:2\n",
      "       0/9     2.048     1.296     4.968     3.774:  91%|######### | 87/96 [00:2\n",
      "       0/9     2.049     1.296     4.962     3.772:  91%|######### | 87/96 [00:2\n",
      "       0/9     2.049     1.296     4.962     3.772:  92%|#########1| 88/96 [00:2\n",
      "       0/9     2.048     1.295     4.952     3.768:  92%|#########1| 88/96 [00:2\n",
      "       0/9     2.048     1.295     4.952     3.768:  93%|#########2| 89/96 [00:2\n",
      "       0/9     2.047     1.293     4.942     3.764:  93%|#########2| 89/96 [00:2\n",
      "       0/9     2.047     1.293     4.942     3.764:  94%|#########3| 90/96 [00:2\n",
      "       0/9     2.049     1.293     4.934     3.759:  94%|#########3| 90/96 [00:2\n",
      "       0/9     2.049     1.293     4.934     3.759:  95%|#########4| 91/96 [00:2\n",
      "       0/9     2.043     1.289     4.925     3.757:  95%|#########4| 91/96 [00:2\n",
      "       0/9     2.043     1.289     4.925     3.757:  96%|#########5| 92/96 [00:2\n",
      "       0/9     2.041     1.287     4.915     3.754:  96%|#########5| 92/96 [00:2\n",
      "       0/9     2.041     1.287     4.915     3.754:  97%|#########6| 93/96 [00:2\n",
      "       0/9     2.042      1.29     4.912     3.753:  97%|#########6| 93/96 [00:2\n",
      "       0/9     2.042      1.29     4.912     3.753:  98%|#########7| 94/96 [00:2\n",
      "       0/9     2.045     1.291     4.906     3.747:  98%|#########7| 94/96 [00:2\n",
      "       0/9     2.045     1.291     4.906     3.747:  99%|#########8| 95/96 [00:2\n",
      "       0/9     2.044     1.292     4.899     3.742:  99%|#########8| 95/96 [00:2\n",
      "       0/9     2.044     1.292     4.899     3.742: 100%|##########| 96/96 [00:2\n",
      "       0/9     2.044     1.292     4.899     3.742: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:01<00:12,  1.84s/it]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:03<00:09,  1.63s/it]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:04<00:07,  1.57s/it]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:06<00:06,  1.52s/it]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:08<00:04,  1.61s/it]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:09<00:03,  1.50s/it]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:10<00:01,  1.40s/it]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:11<00:00,  1.16s/it]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:11<00:00,  1.39s/it]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 0 | mAP@0.5: 0.05168615817819903 | mAP@0.50:0.95: 0.03441512618908785\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       1/9     2.077     1.276     4.105     3.246:   0%|          | 0/96 [00:00\n",
      "       1/9     2.077     1.276     4.105     3.246:   1%|1         | 1/96 [00:00\n",
      "       1/9     2.127     1.238     4.091     3.298:   1%|1         | 1/96 [00:00\n",
      "       1/9     2.127     1.238     4.091     3.298:   2%|2         | 2/96 [00:00\n",
      "       1/9      2.01     1.227     4.122     3.278:   2%|2         | 2/96 [00:00\n",
      "       1/9      2.01     1.227     4.122     3.278:   3%|3         | 3/96 [00:00\n",
      "       1/9     1.961     1.204     4.125     3.306:   3%|3         | 3/96 [00:00\n",
      "       1/9     1.961     1.204     4.125     3.306:   4%|4         | 4/96 [00:00\n",
      "       1/9     1.989     1.196     4.115      3.33:   4%|4         | 4/96 [00:01\n",
      "       1/9     1.989     1.196     4.115      3.33:   5%|5         | 5/96 [00:01\n",
      "       1/9     1.986     1.177     4.123     3.325:   5%|5         | 5/96 [00:01\n",
      "       1/9     1.986     1.177     4.123     3.325:   6%|6         | 6/96 [00:01\n",
      "       1/9     1.954     1.172     4.127     3.315:   6%|6         | 6/96 [00:01\n",
      "       1/9     1.954     1.172     4.127     3.315:   7%|7         | 7/96 [00:01\n",
      "       1/9     1.953     1.163      4.11     3.347:   7%|7         | 7/96 [00:01\n",
      "       1/9     1.953     1.163      4.11     3.347:   8%|8         | 8/96 [00:01\n",
      "       1/9     1.963     1.178     4.117     3.349:   8%|8         | 8/96 [00:02\n",
      "       1/9     1.963     1.178     4.117     3.349:   9%|9         | 9/96 [00:02\n",
      "       1/9      1.94     1.163     4.126     3.363:   9%|9         | 9/96 [00:02\n",
      "       1/9      1.94     1.163     4.126     3.363:  10%|#         | 10/96 [00:0\n",
      "       1/9     1.935      1.16     4.107     3.371:  10%|#         | 10/96 [00:0\n",
      "       1/9     1.935      1.16     4.107     3.371:  11%|#1        | 11/96 [00:0\n",
      "       1/9     1.941     1.169     4.101      3.37:  11%|#1        | 11/96 [00:0\n",
      "       1/9     1.941     1.169     4.101      3.37:  12%|#2        | 12/96 [00:0\n",
      "       1/9     1.951     1.165     4.068     3.358:  12%|#2        | 12/96 [00:0\n",
      "       1/9     1.951     1.165     4.068     3.358:  14%|#3        | 13/96 [00:0\n",
      "       1/9      1.94     1.158     4.059     3.365:  14%|#3        | 13/96 [00:0\n",
      "       1/9      1.94     1.158     4.059     3.365:  15%|#4        | 14/96 [00:0\n",
      "       1/9     1.944     1.156     4.053     3.346:  15%|#4        | 14/96 [00:0\n",
      "       1/9     1.944     1.156     4.053     3.346:  16%|#5        | 15/96 [00:0\n",
      "       1/9     1.944     1.163     4.078     3.333:  16%|#5        | 15/96 [00:0\n",
      "       1/9     1.944     1.163     4.078     3.333:  17%|#6        | 16/96 [00:0\n",
      "       1/9      1.94     1.169     4.074     3.339:  17%|#6        | 16/96 [00:0\n",
      "       1/9      1.94     1.169     4.074     3.339:  18%|#7        | 17/96 [00:0\n",
      "       1/9     1.923      1.16     4.071     3.347:  18%|#7        | 17/96 [00:0\n",
      "       1/9     1.923      1.16     4.071     3.347:  19%|#8        | 18/96 [00:0\n",
      "       1/9     1.908     1.153     4.048     3.351:  19%|#8        | 18/96 [00:0\n",
      "       1/9     1.908     1.153     4.048     3.351:  20%|#9        | 19/96 [00:0\n",
      "       1/9     1.897     1.151     4.039      3.35:  20%|#9        | 19/96 [00:0\n",
      "       1/9     1.897     1.151     4.039      3.35:  21%|##        | 20/96 [00:0\n",
      "       1/9     1.903      1.16     4.059      3.35:  21%|##        | 20/96 [00:0\n",
      "       1/9     1.903      1.16     4.059      3.35:  22%|##1       | 21/96 [00:0\n",
      "       1/9     1.894     1.149     4.047     3.351:  22%|##1       | 21/96 [00:0\n",
      "       1/9     1.894     1.149     4.047     3.351:  23%|##2       | 22/96 [00:0\n",
      "       1/9     1.891     1.147     4.049     3.349:  23%|##2       | 22/96 [00:0\n",
      "       1/9     1.891     1.147     4.049     3.349:  24%|##3       | 23/96 [00:0\n",
      "       1/9     1.878      1.14     4.037     3.353:  24%|##3       | 23/96 [00:0\n",
      "       1/9     1.878      1.14     4.037     3.353:  25%|##5       | 24/96 [00:0\n",
      "       1/9     1.876     1.135     4.035     3.356:  25%|##5       | 24/96 [00:0\n",
      "       1/9     1.876     1.135     4.035     3.356:  26%|##6       | 25/96 [00:0\n",
      "       1/9     1.875     1.132     4.021     3.343:  26%|##6       | 25/96 [00:0\n",
      "       1/9     1.875     1.132     4.021     3.343:  27%|##7       | 26/96 [00:0\n",
      "       1/9     1.884     1.135     4.028     3.337:  27%|##7       | 26/96 [00:0\n",
      "       1/9     1.884     1.135     4.028     3.337:  28%|##8       | 27/96 [00:0\n",
      "       1/9      1.88     1.131     4.016      3.33:  28%|##8       | 27/96 [00:0\n",
      "       1/9      1.88     1.131     4.016      3.33:  29%|##9       | 28/96 [00:0\n",
      "       1/9     1.869     1.125     4.015     3.327:  29%|##9       | 28/96 [00:0\n",
      "       1/9     1.869     1.125     4.015     3.327:  30%|###       | 29/96 [00:0\n",
      "       1/9     1.866     1.124     4.016     3.327:  30%|###       | 29/96 [00:0\n",
      "       1/9     1.866     1.124     4.016     3.327:  31%|###1      | 30/96 [00:0\n",
      "       1/9     1.875     1.124     4.021     3.327:  31%|###1      | 30/96 [00:0\n",
      "       1/9     1.875     1.124     4.021     3.327:  32%|###2      | 31/96 [00:0\n",
      "       1/9     1.881     1.125      4.02     3.321:  32%|###2      | 31/96 [00:0\n",
      "       1/9     1.881     1.125      4.02     3.321:  33%|###3      | 32/96 [00:0\n",
      "       1/9     1.881      1.13     4.026     3.317:  33%|###3      | 32/96 [00:0\n",
      "       1/9     1.881      1.13     4.026     3.317:  34%|###4      | 33/96 [00:0\n",
      "       1/9     1.873     1.124     4.018     3.313:  34%|###4      | 33/96 [00:0\n",
      "       1/9     1.873     1.124     4.018     3.313:  35%|###5      | 34/96 [00:0\n",
      "       1/9     1.877     1.123     4.024     3.316:  35%|###5      | 34/96 [00:0\n",
      "       1/9     1.877     1.123     4.024     3.316:  36%|###6      | 35/96 [00:0\n",
      "       1/9     1.869     1.118     4.014     3.317:  36%|###6      | 35/96 [00:0\n",
      "       1/9     1.869     1.118     4.014     3.317:  38%|###7      | 36/96 [00:0\n",
      "       1/9     1.876      1.12      4.01     3.317:  38%|###7      | 36/96 [00:0\n",
      "       1/9     1.876      1.12      4.01     3.317:  39%|###8      | 37/96 [00:0\n",
      "       1/9     1.874     1.124     4.011      3.31:  39%|###8      | 37/96 [00:0\n",
      "       1/9     1.874     1.124     4.011      3.31:  40%|###9      | 38/96 [00:0\n",
      "       1/9      1.87     1.122     4.004     3.309:  40%|###9      | 38/96 [00:0\n",
      "       1/9      1.87     1.122     4.004     3.309:  41%|####      | 39/96 [00:0\n",
      "       1/9     1.872     1.122     4.003     3.309:  41%|####      | 39/96 [00:1\n",
      "       1/9     1.872     1.122     4.003     3.309:  42%|####1     | 40/96 [00:1\n",
      "       1/9      1.88     1.125     4.006     3.307:  42%|####1     | 40/96 [00:1\n",
      "       1/9      1.88     1.125     4.006     3.307:  43%|####2     | 41/96 [00:1\n",
      "       1/9     1.878     1.126     4.007       3.3:  43%|####2     | 41/96 [00:1\n",
      "       1/9     1.878     1.126     4.007       3.3:  44%|####3     | 42/96 [00:1\n",
      "       1/9      1.88     1.128     4.008     3.297:  44%|####3     | 42/96 [00:1\n",
      "       1/9      1.88     1.128     4.008     3.297:  45%|####4     | 43/96 [00:1\n",
      "       1/9     1.881      1.13     4.008     3.294:  45%|####4     | 43/96 [00:1\n",
      "       1/9     1.881      1.13     4.008     3.294:  46%|####5     | 44/96 [00:1\n",
      "       1/9     1.881     1.129     4.009     3.297:  46%|####5     | 44/96 [00:1\n",
      "       1/9     1.881     1.129     4.009     3.297:  47%|####6     | 45/96 [00:1\n",
      "       1/9     1.878     1.128     4.003     3.291:  47%|####6     | 45/96 [00:1\n",
      "       1/9     1.878     1.128     4.003     3.291:  48%|####7     | 46/96 [00:1\n",
      "       1/9     1.872     1.121     3.998     3.297:  48%|####7     | 46/96 [00:1\n",
      "       1/9     1.872     1.121     3.998     3.297:  49%|####8     | 47/96 [00:1\n",
      "       1/9     1.874     1.121      3.99     3.292:  49%|####8     | 47/96 [00:1\n",
      "       1/9     1.874     1.121      3.99     3.292:  50%|#####     | 48/96 [00:1\n",
      "       1/9     1.873     1.121     3.987     3.283:  50%|#####     | 48/96 [00:1\n",
      "       1/9     1.873     1.121     3.987     3.283:  51%|#####1    | 49/96 [00:1\n",
      "       1/9     1.885     1.122     3.986     3.279:  51%|#####1    | 49/96 [00:1\n",
      "       1/9     1.885     1.122     3.986     3.279:  52%|#####2    | 50/96 [00:1\n",
      "       1/9     1.888     1.121     3.987     3.278:  52%|#####2    | 50/96 [00:1\n",
      "       1/9     1.888     1.121     3.987     3.278:  53%|#####3    | 51/96 [00:1\n",
      "       1/9     1.887      1.12     3.985     3.273:  53%|#####3    | 51/96 [00:1\n",
      "       1/9     1.887      1.12     3.985     3.273:  54%|#####4    | 52/96 [00:1\n",
      "       1/9     1.881      1.12     3.981     3.271:  54%|#####4    | 52/96 [00:1\n",
      "       1/9     1.881      1.12     3.981     3.271:  55%|#####5    | 53/96 [00:1\n",
      "       1/9     1.883     1.115     3.975      3.27:  55%|#####5    | 53/96 [00:1\n",
      "       1/9     1.883     1.115     3.975      3.27:  56%|#####6    | 54/96 [00:1\n",
      "       1/9     1.878     1.112      3.97     3.266:  56%|#####6    | 54/96 [00:1\n",
      "       1/9     1.878     1.112      3.97     3.266:  57%|#####7    | 55/96 [00:1\n",
      "       1/9     1.876     1.113     3.964     3.259:  57%|#####7    | 55/96 [00:1\n",
      "       1/9     1.876     1.113     3.964     3.259:  58%|#####8    | 56/96 [00:1\n",
      "       1/9     1.874     1.112     3.959     3.256:  58%|#####8    | 56/96 [00:1\n",
      "       1/9     1.874     1.112     3.959     3.256:  59%|#####9    | 57/96 [00:1\n",
      "       1/9     1.875     1.111     3.955     3.253:  59%|#####9    | 57/96 [00:1\n",
      "       1/9     1.875     1.111     3.955     3.253:  60%|######    | 58/96 [00:1\n",
      "       1/9     1.873     1.112     3.957     3.248:  60%|######    | 58/96 [00:1\n",
      "       1/9     1.873     1.112     3.957     3.248:  61%|######1   | 59/96 [00:1\n",
      "       1/9     1.868     1.111     3.958     3.247:  61%|######1   | 59/96 [00:1\n",
      "       1/9     1.868     1.111     3.958     3.247:  62%|######2   | 60/96 [00:1\n",
      "       1/9     1.867     1.109     3.955     3.243:  62%|######2   | 60/96 [00:1\n",
      "       1/9     1.867     1.109     3.955     3.243:  64%|######3   | 61/96 [00:1\n",
      "       1/9     1.862     1.108      3.95      3.24:  64%|######3   | 61/96 [00:1\n",
      "       1/9     1.862     1.108      3.95      3.24:  65%|######4   | 62/96 [00:1\n",
      "       1/9     1.862     1.108      3.95     3.236:  65%|######4   | 62/96 [00:1\n",
      "       1/9     1.862     1.108      3.95     3.236:  66%|######5   | 63/96 [00:1\n",
      "       1/9     1.864     1.106     3.944     3.231:  66%|######5   | 63/96 [00:1\n",
      "       1/9     1.864     1.106     3.944     3.231:  67%|######6   | 64/96 [00:1\n",
      "       1/9     1.861     1.104     3.937     3.233:  67%|######6   | 64/96 [00:1\n",
      "       1/9     1.861     1.104     3.937     3.233:  68%|######7   | 65/96 [00:1\n",
      "       1/9     1.861     1.103     3.935     3.232:  68%|######7   | 65/96 [00:1\n",
      "       1/9     1.861     1.103     3.935     3.232:  69%|######8   | 66/96 [00:1\n",
      "       1/9     1.866     1.105     3.936     3.229:  69%|######8   | 66/96 [00:1\n",
      "       1/9     1.866     1.105     3.936     3.229:  70%|######9   | 67/96 [00:1\n",
      "       1/9     1.867     1.109     3.938     3.221:  70%|######9   | 67/96 [00:1\n",
      "       1/9     1.867     1.109     3.938     3.221:  71%|#######   | 68/96 [00:1\n",
      "       1/9     1.865     1.109     3.937      3.22:  71%|#######   | 68/96 [00:1\n",
      "       1/9     1.865     1.109     3.937      3.22:  72%|#######1  | 69/96 [00:1\n",
      "       1/9     1.862     1.106     3.933      3.22:  72%|#######1  | 69/96 [00:1\n",
      "       1/9     1.862     1.106     3.933      3.22:  73%|#######2  | 70/96 [00:1\n",
      "       1/9     1.864     1.107     3.928     3.219:  73%|#######2  | 70/96 [00:1\n",
      "       1/9     1.864     1.107     3.928     3.219:  74%|#######3  | 71/96 [00:1\n",
      "       1/9     1.861     1.106     3.927     3.216:  74%|#######3  | 71/96 [00:1\n",
      "       1/9     1.861     1.106     3.927     3.216:  75%|#######5  | 72/96 [00:1\n",
      "       1/9     1.861     1.106     3.923     3.213:  75%|#######5  | 72/96 [00:1\n",
      "       1/9     1.861     1.106     3.923     3.213:  76%|#######6  | 73/96 [00:1\n",
      "       1/9      1.86     1.104     3.924     3.211:  76%|#######6  | 73/96 [00:1\n",
      "       1/9      1.86     1.104     3.924     3.211:  77%|#######7  | 74/96 [00:1\n",
      "       1/9     1.862     1.106     3.925     3.206:  77%|#######7  | 74/96 [00:1\n",
      "       1/9     1.862     1.106     3.925     3.206:  78%|#######8  | 75/96 [00:1\n",
      "       1/9     1.864     1.107     3.923     3.201:  78%|#######8  | 75/96 [00:1\n",
      "       1/9     1.864     1.107     3.923     3.201:  79%|#######9  | 76/96 [00:1\n",
      "       1/9     1.864     1.106      3.92     3.201:  79%|#######9  | 76/96 [00:1\n",
      "       1/9     1.864     1.106      3.92     3.201:  80%|########  | 77/96 [00:1\n",
      "       1/9     1.866     1.105     3.919     3.204:  80%|########  | 77/96 [00:1\n",
      "       1/9     1.866     1.105     3.919     3.204:  81%|########1 | 78/96 [00:1\n",
      "       1/9     1.866     1.103     3.915     3.202:  81%|########1 | 78/96 [00:1\n",
      "       1/9     1.866     1.103     3.915     3.202:  82%|########2 | 79/96 [00:1\n",
      "       1/9     1.866     1.102     3.915     3.201:  82%|########2 | 79/96 [00:1\n",
      "       1/9     1.866     1.102     3.915     3.201:  83%|########3 | 80/96 [00:1\n",
      "       1/9     1.863     1.104     3.915     3.199:  83%|########3 | 80/96 [00:2\n",
      "       1/9     1.863     1.104     3.915     3.199:  84%|########4 | 81/96 [00:2\n",
      "       1/9     1.864     1.104     3.912     3.197:  84%|########4 | 81/96 [00:2\n",
      "       1/9     1.864     1.104     3.912     3.197:  85%|########5 | 82/96 [00:2\n",
      "       1/9     1.863     1.104     3.907     3.191:  85%|########5 | 82/96 [00:2\n",
      "       1/9     1.863     1.104     3.907     3.191:  86%|########6 | 83/96 [00:2\n",
      "       1/9     1.867     1.104     3.907     3.191:  86%|########6 | 83/96 [00:2\n",
      "       1/9     1.867     1.104     3.907     3.191:  88%|########7 | 84/96 [00:2\n",
      "       1/9     1.864     1.103     3.906     3.187:  88%|########7 | 84/96 [00:2\n",
      "       1/9     1.864     1.103     3.906     3.187:  89%|########8 | 85/96 [00:2\n",
      "       1/9     1.865     1.103     3.902     3.185:  89%|########8 | 85/96 [00:2\n",
      "       1/9     1.865     1.103     3.902     3.185:  90%|########9 | 86/96 [00:2\n",
      "       1/9     1.866     1.102       3.9     3.184:  90%|########9 | 86/96 [00:2\n",
      "       1/9     1.866     1.102       3.9     3.184:  91%|######### | 87/96 [00:2\n",
      "       1/9     1.872     1.105     3.902     3.183:  91%|######### | 87/96 [00:2\n",
      "       1/9     1.872     1.105     3.902     3.183:  92%|#########1| 88/96 [00:2\n",
      "       1/9     1.872     1.106     3.903     3.182:  92%|#########1| 88/96 [00:2\n",
      "       1/9     1.872     1.106     3.903     3.182:  93%|#########2| 89/96 [00:2\n",
      "       1/9     1.871     1.105     3.898     3.179:  93%|#########2| 89/96 [00:2\n",
      "       1/9     1.871     1.105     3.898     3.179:  94%|#########3| 90/96 [00:2\n",
      "       1/9     1.872     1.107     3.898     3.177:  94%|#########3| 90/96 [00:2\n",
      "       1/9     1.872     1.107     3.898     3.177:  95%|#########4| 91/96 [00:2\n",
      "       1/9     1.874     1.106     3.897     3.175:  95%|#########4| 91/96 [00:2\n",
      "       1/9     1.874     1.106     3.897     3.175:  96%|#########5| 92/96 [00:2\n",
      "       1/9     1.876     1.108     3.897     3.174:  96%|#########5| 92/96 [00:2\n",
      "       1/9     1.876     1.108     3.897     3.174:  97%|#########6| 93/96 [00:2\n",
      "       1/9     1.876     1.108     3.896     3.173:  97%|#########6| 93/96 [00:2\n",
      "       1/9     1.876     1.108     3.896     3.173:  98%|#########7| 94/96 [00:2\n",
      "       1/9     1.875     1.108     3.894     3.171:  98%|#########7| 94/96 [00:2\n",
      "       1/9     1.875     1.108     3.894     3.171:  99%|#########8| 95/96 [00:2\n",
      "       1/9     1.874     1.108     3.893      3.17:  99%|#########8| 95/96 [00:2\n",
      "       1/9     1.874     1.108     3.893      3.17: 100%|##########| 96/96 [00:2\n",
      "       1/9     1.874     1.108     3.893      3.17: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:01<00:08,  1.24s/it]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:02<00:06,  1.13s/it]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:03<00:05,  1.11s/it]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:04<00:04,  1.06s/it]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:05<00:03,  1.06s/it]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:06<00:02,  1.03s/it]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:07<00:01,  1.06s/it]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:08<00:00,  1.11it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:08<00:00,  1.01s/it]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 1 | mAP@0.5: 0.08852347026307693 | mAP@0.50:0.95: 0.06470762902984564\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       2/9     1.797     1.122     3.741     3.019:   0%|          | 0/96 [00:00\n",
      "       2/9     1.797     1.122     3.741     3.019:   1%|1         | 1/96 [00:00\n",
      "       2/9     1.831     1.081     3.713     3.037:   1%|1         | 1/96 [00:00\n",
      "       2/9     1.831     1.081     3.713     3.037:   2%|2         | 2/96 [00:00\n",
      "       2/9     1.873     1.126     3.711     3.005:   2%|2         | 2/96 [00:00\n",
      "       2/9     1.873     1.126     3.711     3.005:   3%|3         | 3/96 [00:00\n",
      "       2/9      1.83     1.105     3.725     2.918:   3%|3         | 3/96 [00:00\n",
      "       2/9      1.83     1.105     3.725     2.918:   4%|4         | 4/96 [00:00\n",
      "       2/9     1.847     1.081     3.713     2.918:   4%|4         | 4/96 [00:01\n",
      "       2/9     1.847     1.081     3.713     2.918:   5%|5         | 5/96 [00:01\n",
      "       2/9     1.842     1.071     3.707     2.945:   5%|5         | 5/96 [00:01\n",
      "       2/9     1.842     1.071     3.707     2.945:   6%|6         | 6/96 [00:01\n",
      "       2/9     1.886     1.063     3.753     2.952:   6%|6         | 6/96 [00:01\n",
      "       2/9     1.886     1.063     3.753     2.952:   7%|7         | 7/96 [00:01\n",
      "       2/9     1.907     1.072      3.74     2.928:   7%|7         | 7/96 [00:01\n",
      "       2/9     1.907     1.072      3.74     2.928:   8%|8         | 8/96 [00:01\n",
      "       2/9     1.945     1.099      3.77     2.944:   8%|8         | 8/96 [00:02\n",
      "       2/9     1.945     1.099      3.77     2.944:   9%|9         | 9/96 [00:02\n",
      "       2/9     1.921     1.087     3.758     2.947:   9%|9         | 9/96 [00:02\n",
      "       2/9     1.921     1.087     3.758     2.947:  10%|#         | 10/96 [00:0\n",
      "       2/9     2.005     1.106     3.792     2.951:  10%|#         | 10/96 [00:0\n",
      "       2/9     2.005     1.106     3.792     2.951:  11%|#1        | 11/96 [00:0\n",
      "       2/9     1.993     1.107       3.8     2.924:  11%|#1        | 11/96 [00:0\n",
      "       2/9     1.993     1.107       3.8     2.924:  12%|#2        | 12/96 [00:0\n",
      "       2/9         2     1.121     3.804     2.919:  12%|#2        | 12/96 [00:0\n",
      "       2/9         2     1.121     3.804     2.919:  14%|#3        | 13/96 [00:0\n",
      "       2/9      1.99     1.129     3.823     2.923:  14%|#3        | 13/96 [00:0\n",
      "       2/9      1.99     1.129     3.823     2.923:  15%|#4        | 14/96 [00:0\n",
      "       2/9     1.983     1.126     3.823     2.919:  15%|#4        | 14/96 [00:0\n",
      "       2/9     1.983     1.126     3.823     2.919:  16%|#5        | 15/96 [00:0\n",
      "       2/9     1.964     1.121      3.81     2.929:  16%|#5        | 15/96 [00:0\n",
      "       2/9     1.964     1.121      3.81     2.929:  17%|#6        | 16/96 [00:0\n",
      "       2/9     1.952     1.106       3.8     2.944:  17%|#6        | 16/96 [00:0\n",
      "       2/9     1.952     1.106       3.8     2.944:  18%|#7        | 17/96 [00:0\n",
      "       2/9     1.941     1.107     3.796     2.941:  18%|#7        | 17/96 [00:0\n",
      "       2/9     1.941     1.107     3.796     2.941:  19%|#8        | 18/96 [00:0\n",
      "       2/9     1.942     1.106     3.804     2.954:  19%|#8        | 18/96 [00:0\n",
      "       2/9     1.942     1.106     3.804     2.954:  20%|#9        | 19/96 [00:0\n",
      "       2/9      1.94     1.109       3.8     2.957:  20%|#9        | 19/96 [00:0\n",
      "       2/9      1.94     1.109       3.8     2.957:  21%|##        | 20/96 [00:0\n",
      "       2/9     1.939     1.107     3.797     2.957:  21%|##        | 20/96 [00:0\n",
      "       2/9     1.939     1.107     3.797     2.957:  22%|##1       | 21/96 [00:0\n",
      "       2/9     1.935     1.103     3.785     2.958:  22%|##1       | 21/96 [00:0\n",
      "       2/9     1.935     1.103     3.785     2.958:  23%|##2       | 22/96 [00:0\n",
      "       2/9     1.945     1.108     3.796     2.964:  23%|##2       | 22/96 [00:0\n",
      "       2/9     1.945     1.108     3.796     2.964:  24%|##3       | 23/96 [00:0\n",
      "       2/9     1.938     1.106     3.792     2.955:  24%|##3       | 23/96 [00:0\n",
      "       2/9     1.938     1.106     3.792     2.955:  25%|##5       | 24/96 [00:0\n",
      "       2/9     1.942     1.104     3.777      2.95:  25%|##5       | 24/96 [00:0\n",
      "       2/9     1.942     1.104     3.777      2.95:  26%|##6       | 25/96 [00:0\n",
      "       2/9     1.926     1.098     3.773     2.947:  26%|##6       | 25/96 [00:0\n",
      "       2/9     1.926     1.098     3.773     2.947:  27%|##7       | 26/96 [00:0\n",
      "       2/9     1.929     1.099     3.767     2.948:  27%|##7       | 26/96 [00:0\n",
      "       2/9     1.929     1.099     3.767     2.948:  28%|##8       | 27/96 [00:0\n",
      "       2/9     1.922     1.095     3.766     2.954:  28%|##8       | 27/96 [00:0\n",
      "       2/9     1.922     1.095     3.766     2.954:  29%|##9       | 28/96 [00:0\n",
      "       2/9     1.911     1.095     3.764     2.952:  29%|##9       | 28/96 [00:0\n",
      "       2/9     1.911     1.095     3.764     2.952:  30%|###       | 29/96 [00:0\n",
      "       2/9     1.902     1.088     3.761     2.955:  30%|###       | 29/96 [00:0\n",
      "       2/9     1.902     1.088     3.761     2.955:  31%|###1      | 30/96 [00:0\n",
      "       2/9     1.899     1.086     3.758     2.956:  31%|###1      | 30/96 [00:0\n",
      "       2/9     1.899     1.086     3.758     2.956:  32%|###2      | 31/96 [00:0\n",
      "       2/9     1.906     1.092     3.771     2.957:  32%|###2      | 31/96 [00:0\n",
      "       2/9     1.906     1.092     3.771     2.957:  33%|###3      | 32/96 [00:0\n",
      "       2/9     1.903     1.092     3.771     2.962:  33%|###3      | 32/96 [00:0\n",
      "       2/9     1.903     1.092     3.771     2.962:  34%|###4      | 33/96 [00:0\n",
      "       2/9     1.917     1.094      3.78     2.965:  34%|###4      | 33/96 [00:0\n",
      "       2/9     1.917     1.094      3.78     2.965:  35%|###5      | 34/96 [00:0\n",
      "       2/9     1.928     1.097     3.786     2.967:  35%|###5      | 34/96 [00:0\n",
      "       2/9     1.928     1.097     3.786     2.967:  36%|###6      | 35/96 [00:0\n",
      "       2/9     1.929     1.097     3.784     2.961:  36%|###6      | 35/96 [00:0\n",
      "       2/9     1.929     1.097     3.784     2.961:  38%|###7      | 36/96 [00:0\n",
      "       2/9     1.933     1.098     3.784     2.957:  38%|###7      | 36/96 [00:0\n",
      "       2/9     1.933     1.098     3.784     2.957:  39%|###8      | 37/96 [00:0\n",
      "       2/9     1.928     1.097     3.783     2.962:  39%|###8      | 37/96 [00:0\n",
      "       2/9     1.928     1.097     3.783     2.962:  40%|###9      | 38/96 [00:0\n",
      "       2/9     1.921     1.097      3.79     2.962:  40%|###9      | 38/96 [00:0\n",
      "       2/9     1.921     1.097      3.79     2.962:  41%|####      | 39/96 [00:0\n",
      "       2/9     1.921     1.091     3.783     2.962:  41%|####      | 39/96 [00:0\n",
      "       2/9     1.921     1.091     3.783     2.962:  42%|####1     | 40/96 [00:0\n",
      "       2/9     1.924     1.095     3.784      2.96:  42%|####1     | 40/96 [00:1\n",
      "       2/9     1.924     1.095     3.784      2.96:  43%|####2     | 41/96 [00:1\n",
      "       2/9     1.919     1.092      3.78     2.958:  43%|####2     | 41/96 [00:1\n",
      "       2/9     1.919     1.092      3.78     2.958:  44%|####3     | 42/96 [00:1\n",
      "       2/9     1.929     1.099      3.79     2.953:  44%|####3     | 42/96 [00:1\n",
      "       2/9     1.929     1.099      3.79     2.953:  45%|####4     | 43/96 [00:1\n",
      "       2/9      1.93     1.096     3.792     2.957:  45%|####4     | 43/96 [00:1\n",
      "       2/9      1.93     1.096     3.792     2.957:  46%|####5     | 44/96 [00:1\n",
      "       2/9     1.934     1.099     3.793     2.957:  46%|####5     | 44/96 [00:1\n",
      "       2/9     1.934     1.099     3.793     2.957:  47%|####6     | 45/96 [00:1\n",
      "       2/9     1.929     1.099     3.792     2.953:  47%|####6     | 45/96 [00:1\n",
      "       2/9     1.929     1.099     3.792     2.953:  48%|####7     | 46/96 [00:1\n",
      "       2/9     1.923     1.095     3.789     2.952:  48%|####7     | 46/96 [00:1\n",
      "       2/9     1.923     1.095     3.789     2.952:  49%|####8     | 47/96 [00:1\n",
      "       2/9     1.929     1.099     3.791     2.953:  49%|####8     | 47/96 [00:1\n",
      "       2/9     1.929     1.099     3.791     2.953:  50%|#####     | 48/96 [00:1\n",
      "       2/9     1.927     1.102     3.792     2.955:  50%|#####     | 48/96 [00:1\n",
      "       2/9     1.927     1.102     3.792     2.955:  51%|#####1    | 49/96 [00:1\n",
      "       2/9     1.923     1.098     3.789     2.958:  51%|#####1    | 49/96 [00:1\n",
      "       2/9     1.923     1.098     3.789     2.958:  52%|#####2    | 50/96 [00:1\n",
      "       2/9      1.92     1.099      3.79     2.955:  52%|#####2    | 50/96 [00:1\n",
      "       2/9      1.92     1.099      3.79     2.955:  53%|#####3    | 51/96 [00:1\n",
      "       2/9     1.926     1.098     3.786     2.946:  53%|#####3    | 51/96 [00:1\n",
      "       2/9     1.926     1.098     3.786     2.946:  54%|#####4    | 52/96 [00:1\n",
      "       2/9     1.929     1.097     3.787     2.948:  54%|#####4    | 52/96 [00:1\n",
      "       2/9     1.929     1.097     3.787     2.948:  55%|#####5    | 53/96 [00:1\n",
      "       2/9     1.925     1.092     3.782     2.947:  55%|#####5    | 53/96 [00:1\n",
      "       2/9     1.925     1.092     3.782     2.947:  56%|#####6    | 54/96 [00:1\n",
      "       2/9     1.923     1.091      3.78     2.944:  56%|#####6    | 54/96 [00:1\n",
      "       2/9     1.923     1.091      3.78     2.944:  57%|#####7    | 55/96 [00:1\n",
      "       2/9      1.92     1.087     3.775     2.944:  57%|#####7    | 55/96 [00:1\n",
      "       2/9      1.92     1.087     3.775     2.944:  58%|#####8    | 56/96 [00:1\n",
      "       2/9     1.922     1.087     3.775     2.944:  58%|#####8    | 56/96 [00:1\n",
      "       2/9     1.922     1.087     3.775     2.944:  59%|#####9    | 57/96 [00:1\n",
      "       2/9     1.927     1.085     3.777     2.946:  59%|#####9    | 57/96 [00:1\n",
      "       2/9     1.927     1.085     3.777     2.946:  60%|######    | 58/96 [00:1\n",
      "       2/9     1.931     1.086     3.776     2.948:  60%|######    | 58/96 [00:1\n",
      "       2/9     1.931     1.086     3.776     2.948:  61%|######1   | 59/96 [00:1\n",
      "       2/9     1.935     1.089     3.779     2.946:  61%|######1   | 59/96 [00:1\n",
      "       2/9     1.935     1.089     3.779     2.946:  62%|######2   | 60/96 [00:1\n",
      "       2/9     1.939     1.094      3.78     2.942:  62%|######2   | 60/96 [00:1\n",
      "       2/9     1.939     1.094      3.78     2.942:  64%|######3   | 61/96 [00:1\n",
      "       2/9     1.938     1.091     3.776     2.945:  64%|######3   | 61/96 [00:1\n",
      "       2/9     1.938     1.091     3.776     2.945:  65%|######4   | 62/96 [00:1\n",
      "       2/9     1.932      1.09     3.774     2.943:  65%|######4   | 62/96 [00:1\n",
      "       2/9     1.932      1.09     3.774     2.943:  66%|######5   | 63/96 [00:1\n",
      "       2/9      1.93      1.09     3.772     2.941:  66%|######5   | 63/96 [00:1\n",
      "       2/9      1.93      1.09     3.772     2.941:  67%|######6   | 64/96 [00:1\n",
      "       2/9     1.932     1.091     3.774     2.945:  67%|######6   | 64/96 [00:1\n",
      "       2/9     1.932     1.091     3.774     2.945:  68%|######7   | 65/96 [00:1\n",
      "       2/9     1.927      1.09     3.773     2.945:  68%|######7   | 65/96 [00:1\n",
      "       2/9     1.927      1.09     3.773     2.945:  69%|######8   | 66/96 [00:1\n",
      "       2/9     1.928     1.088      3.77     2.942:  69%|######8   | 66/96 [00:1\n",
      "       2/9     1.928     1.088      3.77     2.942:  70%|######9   | 67/96 [00:1\n",
      "       2/9      1.93      1.09     3.773     2.943:  70%|######9   | 67/96 [00:1\n",
      "       2/9      1.93      1.09     3.773     2.943:  71%|#######   | 68/96 [00:1\n",
      "       2/9     1.927     1.089     3.772     2.942:  71%|#######   | 68/96 [00:1\n",
      "       2/9     1.927     1.089     3.772     2.942:  72%|#######1  | 69/96 [00:1\n",
      "       2/9     1.929      1.09     3.773     2.939:  72%|#######1  | 69/96 [00:1\n",
      "       2/9     1.929      1.09     3.773     2.939:  73%|#######2  | 70/96 [00:1\n",
      "       2/9     1.932     1.087     3.768     2.937:  73%|#######2  | 70/96 [00:1\n",
      "       2/9     1.932     1.087     3.768     2.937:  74%|#######3  | 71/96 [00:1\n",
      "       2/9     1.932     1.084     3.765     2.939:  74%|#######3  | 71/96 [00:1\n",
      "       2/9     1.932     1.084     3.765     2.939:  75%|#######5  | 72/96 [00:1\n",
      "       2/9     1.934     1.086     3.767     2.937:  75%|#######5  | 72/96 [00:1\n",
      "       2/9     1.934     1.086     3.767     2.937:  76%|#######6  | 73/96 [00:1\n",
      "       2/9     1.931     1.087     3.769     2.934:  76%|#######6  | 73/96 [00:1\n",
      "       2/9     1.931     1.087     3.769     2.934:  77%|#######7  | 74/96 [00:1\n",
      "       2/9     1.929     1.088     3.768     2.931:  77%|#######7  | 74/96 [00:1\n",
      "       2/9     1.929     1.088     3.768     2.931:  78%|#######8  | 75/96 [00:1\n",
      "       2/9      1.93     1.088     3.769     2.929:  78%|#######8  | 75/96 [00:1\n",
      "       2/9      1.93     1.088     3.769     2.929:  79%|#######9  | 76/96 [00:1\n",
      "       2/9     1.928     1.087     3.769     2.928:  79%|#######9  | 76/96 [00:1\n",
      "       2/9     1.928     1.087     3.769     2.928:  80%|########  | 77/96 [00:1\n",
      "       2/9     1.933      1.09     3.771     2.923:  80%|########  | 77/96 [00:1\n",
      "       2/9     1.933      1.09     3.771     2.923:  81%|########1 | 78/96 [00:1\n",
      "       2/9      1.93     1.088     3.772     2.924:  81%|########1 | 78/96 [00:1\n",
      "       2/9      1.93     1.088     3.772     2.924:  82%|########2 | 79/96 [00:1\n",
      "       2/9     1.927     1.088     3.768     2.921:  82%|########2 | 79/96 [00:1\n",
      "       2/9     1.927     1.088     3.768     2.921:  83%|########3 | 80/96 [00:1\n",
      "       2/9     1.924     1.088     3.766     2.919:  83%|########3 | 80/96 [00:1\n",
      "       2/9     1.924     1.088     3.766     2.919:  84%|########4 | 81/96 [00:1\n",
      "       2/9     1.924      1.09     3.767     2.914:  84%|########4 | 81/96 [00:1\n",
      "       2/9     1.924      1.09     3.767     2.914:  85%|########5 | 82/96 [00:1\n",
      "       2/9     1.922      1.09     3.763     2.911:  85%|########5 | 82/96 [00:2\n",
      "       2/9     1.922      1.09     3.763     2.911:  86%|########6 | 83/96 [00:2\n",
      "       2/9     1.922     1.089     3.761      2.91:  86%|########6 | 83/96 [00:2\n",
      "       2/9     1.922     1.089     3.761      2.91:  88%|########7 | 84/96 [00:2\n",
      "       2/9     1.921      1.09     3.763     2.906:  88%|########7 | 84/96 [00:2\n",
      "       2/9     1.921      1.09     3.763     2.906:  89%|########8 | 85/96 [00:2\n",
      "       2/9     1.918      1.09     3.761     2.908:  89%|########8 | 85/96 [00:2\n",
      "       2/9     1.918      1.09     3.761     2.908:  90%|########9 | 86/96 [00:2\n",
      "       2/9     1.917      1.09     3.762     2.904:  90%|########9 | 86/96 [00:2\n",
      "       2/9     1.917      1.09     3.762     2.904:  91%|######### | 87/96 [00:2\n",
      "       2/9     1.917     1.089     3.761     2.904:  91%|######### | 87/96 [00:2\n",
      "       2/9     1.917     1.089     3.761     2.904:  92%|#########1| 88/96 [00:2\n",
      "       2/9     1.914     1.086     3.759     2.907:  92%|#########1| 88/96 [00:2\n",
      "       2/9     1.914     1.086     3.759     2.907:  93%|#########2| 89/96 [00:2\n",
      "       2/9     1.915     1.087     3.758     2.906:  93%|#########2| 89/96 [00:2\n",
      "       2/9     1.915     1.087     3.758     2.906:  94%|#########3| 90/96 [00:2\n",
      "       2/9     1.912     1.087     3.758     2.904:  94%|#########3| 90/96 [00:2\n",
      "       2/9     1.912     1.087     3.758     2.904:  95%|#########4| 91/96 [00:2\n",
      "       2/9     1.908     1.084     3.754     2.906:  95%|#########4| 91/96 [00:2\n",
      "       2/9     1.908     1.084     3.754     2.906:  96%|#########5| 92/96 [00:2\n",
      "       2/9     1.908     1.084     3.753     2.907:  96%|#########5| 92/96 [00:2\n",
      "       2/9     1.908     1.084     3.753     2.907:  97%|#########6| 93/96 [00:2\n",
      "       2/9     1.906     1.083     3.752     2.908:  97%|#########6| 93/96 [00:2\n",
      "       2/9     1.906     1.083     3.752     2.908:  98%|#########7| 94/96 [00:2\n",
      "       2/9     1.905     1.083     3.751     2.908:  98%|#########7| 94/96 [00:2\n",
      "       2/9     1.905     1.083     3.751     2.908:  99%|#########8| 95/96 [00:2\n",
      "       2/9     1.906     1.085      3.75     2.905:  99%|#########8| 95/96 [00:2\n",
      "       2/9     1.906     1.085      3.75     2.905: 100%|##########| 96/96 [00:2\n",
      "       2/9     1.906     1.085      3.75     2.905: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:01<00:07,  1.05s/it]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:05,  1.09it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.06it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.11it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:04<00:02,  1.15it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:05<00:01,  1.19it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:06<00:00,  1.14it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.34it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.19it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 2 | mAP@0.5: 0.10800772376436932 | mAP@0.50:0.95: 0.0807155138709978\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       3/9     1.736    0.9967     3.658     2.763:   0%|          | 0/96 [00:00\n",
      "       3/9     1.736    0.9967     3.658     2.763:   1%|1         | 1/96 [00:00\n",
      "       3/9     1.825    0.9955     3.865     2.663:   1%|1         | 1/96 [00:00\n",
      "       3/9     1.825    0.9955     3.865     2.663:   2%|2         | 2/96 [00:00\n",
      "       3/9     1.979      1.07     3.822     2.689:   2%|2         | 2/96 [00:00\n",
      "       3/9     1.979      1.07     3.822     2.689:   3%|3         | 3/96 [00:00\n",
      "       3/9     1.871     1.025     3.739     2.719:   3%|3         | 3/96 [00:00\n",
      "       3/9     1.871     1.025     3.739     2.719:   4%|4         | 4/96 [00:00\n",
      "       3/9     1.811    0.9951     3.678     2.734:   4%|4         | 4/96 [00:01\n",
      "       3/9     1.811    0.9951     3.678     2.734:   5%|5         | 5/96 [00:01\n",
      "       3/9       1.8    0.9929     3.681     2.754:   5%|5         | 5/96 [00:01\n",
      "       3/9       1.8    0.9929     3.681     2.754:   6%|6         | 6/96 [00:01\n",
      "       3/9     1.807     1.011     3.692     2.744:   6%|6         | 6/96 [00:01\n",
      "       3/9     1.807     1.011     3.692     2.744:   7%|7         | 7/96 [00:01\n",
      "       3/9     1.809     1.013     3.695     2.734:   7%|7         | 7/96 [00:01\n",
      "       3/9     1.809     1.013     3.695     2.734:   8%|8         | 8/96 [00:01\n",
      "       3/9     1.812     1.019       3.7     2.727:   8%|8         | 8/96 [00:02\n",
      "       3/9     1.812     1.019       3.7     2.727:   9%|9         | 9/96 [00:02\n",
      "       3/9     1.826     1.021     3.694     2.748:   9%|9         | 9/96 [00:02\n",
      "       3/9     1.826     1.021     3.694     2.748:  10%|#         | 10/96 [00:0\n",
      "       3/9     1.836     1.036     3.695     2.746:  10%|#         | 10/96 [00:0\n",
      "       3/9     1.836     1.036     3.695     2.746:  11%|#1        | 11/96 [00:0\n",
      "       3/9     1.815     1.026     3.696     2.745:  11%|#1        | 11/96 [00:0\n",
      "       3/9     1.815     1.026     3.696     2.745:  12%|#2        | 12/96 [00:0\n",
      "       3/9     1.801     1.021     3.681     2.756:  12%|#2        | 12/96 [00:0\n",
      "       3/9     1.801     1.021     3.681     2.756:  14%|#3        | 13/96 [00:0\n",
      "       3/9     1.796      1.02     3.668     2.757:  14%|#3        | 13/96 [00:0\n",
      "       3/9     1.796      1.02     3.668     2.757:  15%|#4        | 14/96 [00:0\n",
      "       3/9       1.8     1.025     3.682     2.758:  15%|#4        | 14/96 [00:0\n",
      "       3/9       1.8     1.025     3.682     2.758:  16%|#5        | 15/96 [00:0\n",
      "       3/9     1.802     1.018     3.672     2.753:  16%|#5        | 15/96 [00:0\n",
      "       3/9     1.802     1.018     3.672     2.753:  17%|#6        | 16/96 [00:0\n",
      "       3/9     1.813     1.021     3.673     2.764:  17%|#6        | 16/96 [00:0\n",
      "       3/9     1.813     1.021     3.673     2.764:  18%|#7        | 17/96 [00:0\n",
      "       3/9     1.808     1.016     3.669     2.749:  18%|#7        | 17/96 [00:0\n",
      "       3/9     1.808     1.016     3.669     2.749:  19%|#8        | 18/96 [00:0\n",
      "       3/9     1.805     1.017     3.667      2.75:  19%|#8        | 18/96 [00:0\n",
      "       3/9     1.805     1.017     3.667      2.75:  20%|#9        | 19/96 [00:0\n",
      "       3/9     1.815     1.032     3.678     2.746:  20%|#9        | 19/96 [00:0\n",
      "       3/9     1.815     1.032     3.678     2.746:  21%|##        | 20/96 [00:0\n",
      "       3/9     1.815     1.029     3.664     2.737:  21%|##        | 20/96 [00:0\n",
      "       3/9     1.815     1.029     3.664     2.737:  22%|##1       | 21/96 [00:0\n",
      "       3/9     1.804     1.027     3.668      2.74:  22%|##1       | 21/96 [00:0\n",
      "       3/9     1.804     1.027     3.668      2.74:  23%|##2       | 22/96 [00:0\n",
      "       3/9     1.803     1.032     3.674     2.739:  23%|##2       | 22/96 [00:0\n",
      "       3/9     1.803     1.032     3.674     2.739:  24%|##3       | 23/96 [00:0\n",
      "       3/9     1.803      1.03     3.672      2.75:  24%|##3       | 23/96 [00:0\n",
      "       3/9     1.803      1.03     3.672      2.75:  25%|##5       | 24/96 [00:0\n",
      "       3/9     1.799     1.027     3.668     2.754:  25%|##5       | 24/96 [00:0\n",
      "       3/9     1.799     1.027     3.668     2.754:  26%|##6       | 25/96 [00:0\n",
      "       3/9     1.798      1.03     3.662     2.753:  26%|##6       | 25/96 [00:0\n",
      "       3/9     1.798      1.03     3.662     2.753:  27%|##7       | 26/96 [00:0\n",
      "       3/9     1.803     1.031     3.655     2.748:  27%|##7       | 26/96 [00:0\n",
      "       3/9     1.803     1.031     3.655     2.748:  28%|##8       | 27/96 [00:0\n",
      "       3/9     1.809      1.04     3.658     2.745:  28%|##8       | 27/96 [00:0\n",
      "       3/9     1.809      1.04     3.658     2.745:  29%|##9       | 28/96 [00:0\n",
      "       3/9      1.81     1.037     3.654     2.752:  29%|##9       | 28/96 [00:0\n",
      "       3/9      1.81     1.037     3.654     2.752:  30%|###       | 29/96 [00:0\n",
      "       3/9     1.823      1.04      3.66     2.755:  30%|###       | 29/96 [00:0\n",
      "       3/9     1.823      1.04      3.66     2.755:  31%|###1      | 30/96 [00:0\n",
      "       3/9     1.819     1.043     3.662     2.753:  31%|###1      | 30/96 [00:0\n",
      "       3/9     1.819     1.043     3.662     2.753:  32%|###2      | 31/96 [00:0\n",
      "       3/9     1.826     1.045     3.673     2.742:  32%|###2      | 31/96 [00:0\n",
      "       3/9     1.826     1.045     3.673     2.742:  33%|###3      | 32/96 [00:0\n",
      "       3/9      1.83     1.049     3.673     2.749:  33%|###3      | 32/96 [00:0\n",
      "       3/9      1.83     1.049     3.673     2.749:  34%|###4      | 33/96 [00:0\n",
      "       3/9     1.823     1.047     3.672     2.744:  34%|###4      | 33/96 [00:0\n",
      "       3/9     1.823     1.047     3.672     2.744:  35%|###5      | 34/96 [00:0\n",
      "       3/9     1.831     1.052     3.667     2.734:  35%|###5      | 34/96 [00:0\n",
      "       3/9     1.831     1.052     3.667     2.734:  36%|###6      | 35/96 [00:0\n",
      "       3/9     1.818     1.045     3.657      2.73:  36%|###6      | 35/96 [00:0\n",
      "       3/9     1.818     1.045     3.657      2.73:  38%|###7      | 36/96 [00:0\n",
      "       3/9     1.824     1.041     3.651     2.734:  38%|###7      | 36/96 [00:0\n",
      "       3/9     1.824     1.041     3.651     2.734:  39%|###8      | 37/96 [00:0\n",
      "       3/9     1.828     1.038     3.649     2.732:  39%|###8      | 37/96 [00:0\n",
      "       3/9     1.828     1.038     3.649     2.732:  40%|###9      | 38/96 [00:0\n",
      "       3/9     1.825     1.038     3.644     2.729:  40%|###9      | 38/96 [00:0\n",
      "       3/9     1.825     1.038     3.644     2.729:  41%|####      | 39/96 [00:0\n",
      "       3/9     1.822     1.036     3.645     2.735:  41%|####      | 39/96 [00:0\n",
      "       3/9     1.822     1.036     3.645     2.735:  42%|####1     | 40/96 [00:0\n",
      "       3/9     1.815     1.032     3.631     2.732:  42%|####1     | 40/96 [00:0\n",
      "       3/9     1.815     1.032     3.631     2.732:  43%|####2     | 41/96 [00:0\n",
      "       3/9     1.818     1.034     3.624      2.73:  43%|####2     | 41/96 [00:1\n",
      "       3/9     1.818     1.034     3.624      2.73:  44%|####3     | 42/96 [00:1\n",
      "       3/9     1.818     1.032      3.62     2.732:  44%|####3     | 42/96 [00:1\n",
      "       3/9     1.818     1.032      3.62     2.732:  45%|####4     | 43/96 [00:1\n",
      "       3/9     1.821     1.033     3.622     2.736:  45%|####4     | 43/96 [00:1\n",
      "       3/9     1.821     1.033     3.622     2.736:  46%|####5     | 44/96 [00:1\n",
      "       3/9     1.818     1.032     3.622     2.737:  46%|####5     | 44/96 [00:1\n",
      "       3/9     1.818     1.032     3.622     2.737:  47%|####6     | 45/96 [00:1\n",
      "       3/9     1.817     1.035     3.627     2.733:  47%|####6     | 45/96 [00:1\n",
      "       3/9     1.817     1.035     3.627     2.733:  48%|####7     | 46/96 [00:1\n",
      "       3/9     1.819     1.035     3.624     2.728:  48%|####7     | 46/96 [00:1\n",
      "       3/9     1.819     1.035     3.624     2.728:  49%|####8     | 47/96 [00:1\n",
      "       3/9     1.818     1.034     3.622     2.725:  49%|####8     | 47/96 [00:1\n",
      "       3/9     1.818     1.034     3.622     2.725:  50%|#####     | 48/96 [00:1\n",
      "       3/9     1.822     1.033     3.628     2.723:  50%|#####     | 48/96 [00:1\n",
      "       3/9     1.822     1.033     3.628     2.723:  51%|#####1    | 49/96 [00:1\n",
      "       3/9     1.819     1.031     3.622     2.722:  51%|#####1    | 49/96 [00:1\n",
      "       3/9     1.819     1.031     3.622     2.722:  52%|#####2    | 50/96 [00:1\n",
      "       3/9     1.824     1.032      3.63     2.722:  52%|#####2    | 50/96 [00:1\n",
      "       3/9     1.824     1.032      3.63     2.722:  53%|#####3    | 51/96 [00:1\n",
      "       3/9     1.825     1.032     3.629     2.721:  53%|#####3    | 51/96 [00:1\n",
      "       3/9     1.825     1.032     3.629     2.721:  54%|#####4    | 52/96 [00:1\n",
      "       3/9     1.823      1.03      3.63     2.718:  54%|#####4    | 52/96 [00:1\n",
      "       3/9     1.823      1.03      3.63     2.718:  55%|#####5    | 53/96 [00:1\n",
      "       3/9     1.825      1.03     3.626     2.716:  55%|#####5    | 53/96 [00:1\n",
      "       3/9     1.825      1.03     3.626     2.716:  56%|#####6    | 54/96 [00:1\n",
      "       3/9     1.826      1.03     3.626     2.714:  56%|#####6    | 54/96 [00:1\n",
      "       3/9     1.826      1.03     3.626     2.714:  57%|#####7    | 55/96 [00:1\n",
      "       3/9     1.836     1.029     3.627     2.713:  57%|#####7    | 55/96 [00:1\n",
      "       3/9     1.836     1.029     3.627     2.713:  58%|#####8    | 56/96 [00:1\n",
      "       3/9     1.834     1.028     3.624     2.717:  58%|#####8    | 56/96 [00:1\n",
      "       3/9     1.834     1.028     3.624     2.717:  59%|#####9    | 57/96 [00:1\n",
      "       3/9     1.833      1.03     3.629     2.717:  59%|#####9    | 57/96 [00:1\n",
      "       3/9     1.833      1.03     3.629     2.717:  60%|######    | 58/96 [00:1\n",
      "       3/9     1.831      1.03     3.626     2.717:  60%|######    | 58/96 [00:1\n",
      "       3/9     1.831      1.03     3.626     2.717:  61%|######1   | 59/96 [00:1\n",
      "       3/9     1.832      1.03     3.622     2.715:  61%|######1   | 59/96 [00:1\n",
      "       3/9     1.832      1.03     3.622     2.715:  62%|######2   | 60/96 [00:1\n",
      "       3/9     1.841     1.031     3.624     2.718:  62%|######2   | 60/96 [00:1\n",
      "       3/9     1.841     1.031     3.624     2.718:  64%|######3   | 61/96 [00:1\n",
      "       3/9     1.839      1.03     3.625     2.716:  64%|######3   | 61/96 [00:1\n",
      "       3/9     1.839      1.03     3.625     2.716:  65%|######4   | 62/96 [00:1\n",
      "       3/9      1.84     1.029      3.62     2.713:  65%|######4   | 62/96 [00:1\n",
      "       3/9      1.84     1.029      3.62     2.713:  66%|######5   | 63/96 [00:1\n",
      "       3/9     1.841     1.031     3.619     2.712:  66%|######5   | 63/96 [00:1\n",
      "       3/9     1.841     1.031     3.619     2.712:  67%|######6   | 64/96 [00:1\n",
      "       3/9     1.841      1.03     3.622     2.715:  67%|######6   | 64/96 [00:1\n",
      "       3/9     1.841      1.03     3.622     2.715:  68%|######7   | 65/96 [00:1\n",
      "       3/9     1.838     1.029     3.616     2.714:  68%|######7   | 65/96 [00:1\n",
      "       3/9     1.838     1.029     3.616     2.714:  69%|######8   | 66/96 [00:1\n",
      "       3/9     1.842     1.029     3.618     2.716:  69%|######8   | 66/96 [00:1\n",
      "       3/9     1.842     1.029     3.618     2.716:  70%|######9   | 67/96 [00:1\n",
      "       3/9     1.845     1.031     3.621     2.718:  70%|######9   | 67/96 [00:1\n",
      "       3/9     1.845     1.031     3.621     2.718:  71%|#######   | 68/96 [00:1\n",
      "       3/9     1.849     1.031      3.62     2.715:  71%|#######   | 68/96 [00:1\n",
      "       3/9     1.849     1.031      3.62     2.715:  72%|#######1  | 69/96 [00:1\n",
      "       3/9     1.843     1.028     3.618     2.717:  72%|#######1  | 69/96 [00:1\n",
      "       3/9     1.843     1.028     3.618     2.717:  73%|#######2  | 70/96 [00:1\n",
      "       3/9     1.837     1.026     3.614     2.712:  73%|#######2  | 70/96 [00:1\n",
      "       3/9     1.837     1.026     3.614     2.712:  74%|#######3  | 71/96 [00:1\n",
      "       3/9     1.843     1.027     3.614     2.712:  74%|#######3  | 71/96 [00:1\n",
      "       3/9     1.843     1.027     3.614     2.712:  75%|#######5  | 72/96 [00:1\n",
      "       3/9     1.844     1.027     3.611     2.712:  75%|#######5  | 72/96 [00:1\n",
      "       3/9     1.844     1.027     3.611     2.712:  76%|#######6  | 73/96 [00:1\n",
      "       3/9     1.845     1.024     3.606     2.713:  76%|#######6  | 73/96 [00:1\n",
      "       3/9     1.845     1.024     3.606     2.713:  77%|#######7  | 74/96 [00:1\n",
      "       3/9     1.843     1.026     3.605     2.711:  77%|#######7  | 74/96 [00:1\n",
      "       3/9     1.843     1.026     3.605     2.711:  78%|#######8  | 75/96 [00:1\n",
      "       3/9     1.841     1.027     3.603     2.712:  78%|#######8  | 75/96 [00:1\n",
      "       3/9     1.841     1.027     3.603     2.712:  79%|#######9  | 76/96 [00:1\n",
      "       3/9     1.839     1.026     3.603     2.717:  79%|#######9  | 76/96 [00:1\n",
      "       3/9     1.839     1.026     3.603     2.717:  80%|########  | 77/96 [00:1\n",
      "       3/9      1.84     1.024       3.6     2.713:  80%|########  | 77/96 [00:1\n",
      "       3/9      1.84     1.024       3.6     2.713:  81%|########1 | 78/96 [00:1\n",
      "       3/9     1.838     1.022     3.597      2.71:  81%|########1 | 78/96 [00:1\n",
      "       3/9     1.838     1.022     3.597      2.71:  82%|########2 | 79/96 [00:1\n",
      "       3/9     1.837     1.021     3.596     2.709:  82%|########2 | 79/96 [00:1\n",
      "       3/9     1.837     1.021     3.596     2.709:  83%|########3 | 80/96 [00:1\n",
      "       3/9     1.835     1.023     3.599     2.709:  83%|########3 | 80/96 [00:1\n",
      "       3/9     1.835     1.023     3.599     2.709:  84%|########4 | 81/96 [00:1\n",
      "       3/9     1.832     1.021     3.596     2.708:  84%|########4 | 81/96 [00:1\n",
      "       3/9     1.832     1.021     3.596     2.708:  85%|########5 | 82/96 [00:1\n",
      "       3/9     1.826     1.019     3.591     2.708:  85%|########5 | 82/96 [00:1\n",
      "       3/9     1.826     1.019     3.591     2.708:  86%|########6 | 83/96 [00:1\n",
      "       3/9     1.828      1.02     3.591      2.71:  86%|########6 | 83/96 [00:2\n",
      "       3/9     1.828      1.02     3.591      2.71:  88%|########7 | 84/96 [00:2\n",
      "       3/9     1.828     1.021     3.592     2.706:  88%|########7 | 84/96 [00:2\n",
      "       3/9     1.828     1.021     3.592     2.706:  89%|########8 | 85/96 [00:2\n",
      "       3/9      1.83     1.021      3.59     2.705:  89%|########8 | 85/96 [00:2\n",
      "       3/9      1.83     1.021      3.59     2.705:  90%|########9 | 86/96 [00:2\n",
      "       3/9     1.828     1.019     3.586     2.701:  90%|########9 | 86/96 [00:2\n",
      "       3/9     1.828     1.019     3.586     2.701:  91%|######### | 87/96 [00:2\n",
      "       3/9     1.826     1.017     3.582     2.698:  91%|######### | 87/96 [00:2\n",
      "       3/9     1.826     1.017     3.582     2.698:  92%|#########1| 88/96 [00:2\n",
      "       3/9     1.826     1.019     3.585     2.698:  92%|#########1| 88/96 [00:2\n",
      "       3/9     1.826     1.019     3.585     2.698:  93%|#########2| 89/96 [00:2\n",
      "       3/9     1.829     1.021     3.585     2.697:  93%|#########2| 89/96 [00:2\n",
      "       3/9     1.829     1.021     3.585     2.697:  94%|#########3| 90/96 [00:2\n",
      "       3/9     1.826      1.02     3.583     2.696:  94%|#########3| 90/96 [00:2\n",
      "       3/9     1.826      1.02     3.583     2.696:  95%|#########4| 91/96 [00:2\n",
      "       3/9     1.827      1.02     3.586     2.695:  95%|#########4| 91/96 [00:2\n",
      "       3/9     1.827      1.02     3.586     2.695:  96%|#########5| 92/96 [00:2\n",
      "       3/9     1.827      1.02     3.584     2.692:  96%|#########5| 92/96 [00:2\n",
      "       3/9     1.827      1.02     3.584     2.692:  97%|#########6| 93/96 [00:2\n",
      "       3/9     1.828      1.02     3.585     2.693:  97%|#########6| 93/96 [00:2\n",
      "       3/9     1.828      1.02     3.585     2.693:  98%|#########7| 94/96 [00:2\n",
      "       3/9     1.824     1.018     3.584     2.692:  98%|#########7| 94/96 [00:2\n",
      "       3/9     1.824     1.018     3.584     2.692:  99%|#########8| 95/96 [00:2\n",
      "       3/9     1.823     1.016     3.579     2.695:  99%|#########8| 95/96 [00:2\n",
      "       3/9     1.823     1.016     3.579     2.695: 100%|##########| 96/96 [00:2\n",
      "       3/9     1.823     1.016     3.579     2.695: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:00<00:06,  1.04it/s]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:05,  1.17it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.13it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.17it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:04<00:02,  1.21it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:04<00:01,  1.26it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:05<00:00,  1.19it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.42it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.26it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 3 | mAP@0.5: 0.1603332604420155 | mAP@0.50:0.95: 0.1209720184146251\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       4/9     1.589    0.8901     3.395      2.82:   0%|          | 0/96 [00:00\n",
      "       4/9     1.589    0.8901     3.395      2.82:   1%|1         | 1/96 [00:00\n",
      "       4/9     1.559    0.8433       3.5     2.859:   1%|1         | 1/96 [00:00\n",
      "       4/9     1.559    0.8433       3.5     2.859:   2%|2         | 2/96 [00:00\n",
      "       4/9     1.567    0.8895     3.586     2.804:   2%|2         | 2/96 [00:00\n",
      "       4/9     1.567    0.8895     3.586     2.804:   3%|3         | 3/96 [00:00\n",
      "       4/9     1.603    0.8958     3.578     2.765:   3%|3         | 3/96 [00:00\n",
      "       4/9     1.603    0.8958     3.578     2.765:   4%|4         | 4/96 [00:00\n",
      "       4/9     1.643    0.9477     3.603     2.698:   4%|4         | 4/96 [00:01\n",
      "       4/9     1.643    0.9477     3.603     2.698:   5%|5         | 5/96 [00:01\n",
      "       4/9     1.708    0.9604      3.62     2.685:   5%|5         | 5/96 [00:01\n",
      "       4/9     1.708    0.9604      3.62     2.685:   6%|6         | 6/96 [00:01\n",
      "       4/9     1.685    0.9542     3.572     2.665:   6%|6         | 6/96 [00:01\n",
      "       4/9     1.685    0.9542     3.572     2.665:   7%|7         | 7/96 [00:01\n",
      "       4/9     1.732    0.9797     3.596     2.629:   7%|7         | 7/96 [00:01\n",
      "       4/9     1.732    0.9797     3.596     2.629:   8%|8         | 8/96 [00:01\n",
      "       4/9      1.76    0.9759     3.596     2.634:   8%|8         | 8/96 [00:02\n",
      "       4/9      1.76    0.9759     3.596     2.634:   9%|9         | 9/96 [00:02\n",
      "       4/9     1.784    0.9858       3.6     2.657:   9%|9         | 9/96 [00:02\n",
      "       4/9     1.784    0.9858       3.6     2.657:  10%|#         | 10/96 [00:0\n",
      "       4/9     1.794    0.9817     3.585     2.662:  10%|#         | 10/96 [00:0\n",
      "       4/9     1.794    0.9817     3.585     2.662:  11%|#1        | 11/96 [00:0\n",
      "       4/9     1.782    0.9818     3.574     2.655:  11%|#1        | 11/96 [00:0\n",
      "       4/9     1.782    0.9818     3.574     2.655:  12%|#2        | 12/96 [00:0\n",
      "       4/9     1.767     0.986     3.584     2.638:  12%|#2        | 12/96 [00:0\n",
      "       4/9     1.767     0.986     3.584     2.638:  14%|#3        | 13/96 [00:0\n",
      "       4/9     1.758    0.9792     3.548     2.625:  14%|#3        | 13/96 [00:0\n",
      "       4/9     1.758    0.9792     3.548     2.625:  15%|#4        | 14/96 [00:0\n",
      "       4/9     1.755    0.9764     3.544     2.635:  15%|#4        | 14/96 [00:0\n",
      "       4/9     1.755    0.9764     3.544     2.635:  16%|#5        | 15/96 [00:0\n",
      "       4/9     1.765    0.9812     3.553     2.626:  16%|#5        | 15/96 [00:0\n",
      "       4/9     1.765    0.9812     3.553     2.626:  17%|#6        | 16/96 [00:0\n",
      "       4/9      1.77    0.9886     3.547     2.622:  17%|#6        | 16/96 [00:0\n",
      "       4/9      1.77    0.9886     3.547     2.622:  18%|#7        | 17/96 [00:0\n",
      "       4/9     1.784    0.9928     3.552     2.621:  18%|#7        | 17/96 [00:0\n",
      "       4/9     1.784    0.9928     3.552     2.621:  19%|#8        | 18/96 [00:0\n",
      "       4/9     1.792    0.9989     3.559     2.622:  19%|#8        | 18/96 [00:0\n",
      "       4/9     1.792    0.9989     3.559     2.622:  20%|#9        | 19/96 [00:0\n",
      "       4/9     1.805     1.003     3.552     2.626:  20%|#9        | 19/96 [00:0\n",
      "       4/9     1.805     1.003     3.552     2.626:  21%|##        | 20/96 [00:0\n",
      "       4/9     1.799    0.9973     3.546     2.629:  21%|##        | 20/96 [00:0\n",
      "       4/9     1.799    0.9973     3.546     2.629:  22%|##1       | 21/96 [00:0\n",
      "       4/9     1.801     1.002     3.552     2.623:  22%|##1       | 21/96 [00:0\n",
      "       4/9     1.801     1.002     3.552     2.623:  23%|##2       | 22/96 [00:0\n",
      "       4/9     1.801    0.9962     3.541     2.619:  23%|##2       | 22/96 [00:0\n",
      "       4/9     1.801    0.9962     3.541     2.619:  24%|##3       | 23/96 [00:0\n",
      "       4/9     1.818     1.001     3.541     2.617:  24%|##3       | 23/96 [00:0\n",
      "       4/9     1.818     1.001     3.541     2.617:  25%|##5       | 24/96 [00:0\n",
      "       4/9      1.81    0.9976     3.548     2.618:  25%|##5       | 24/96 [00:0\n",
      "       4/9      1.81    0.9976     3.548     2.618:  26%|##6       | 25/96 [00:0\n",
      "       4/9     1.817     0.995     3.549     2.623:  26%|##6       | 25/96 [00:0\n",
      "       4/9     1.817     0.995     3.549     2.623:  27%|##7       | 26/96 [00:0\n",
      "       4/9      1.82         1     3.551     2.615:  27%|##7       | 26/96 [00:0\n",
      "       4/9      1.82         1     3.551     2.615:  28%|##8       | 27/96 [00:0\n",
      "       4/9     1.829     1.004     3.559     2.625:  28%|##8       | 27/96 [00:0\n",
      "       4/9     1.829     1.004     3.559     2.625:  29%|##9       | 28/96 [00:0\n",
      "       4/9      1.83     1.001     3.559     2.624:  29%|##9       | 28/96 [00:0\n",
      "       4/9      1.83     1.001     3.559     2.624:  30%|###       | 29/96 [00:0\n",
      "       4/9     1.828     1.003     3.561     2.622:  30%|###       | 29/96 [00:0\n",
      "       4/9     1.828     1.003     3.561     2.622:  31%|###1      | 30/96 [00:0\n",
      "       4/9     1.838     1.012     3.565     2.625:  31%|###1      | 30/96 [00:0\n",
      "       4/9     1.838     1.012     3.565     2.625:  32%|###2      | 31/96 [00:0\n",
      "       4/9     1.838      1.01     3.558     2.624:  32%|###2      | 31/96 [00:0\n",
      "       4/9     1.838      1.01     3.558     2.624:  33%|###3      | 32/96 [00:0\n",
      "       4/9     1.835     1.012     3.559     2.618:  33%|###3      | 32/96 [00:0\n",
      "       4/9     1.835     1.012     3.559     2.618:  34%|###4      | 33/96 [00:0\n",
      "       4/9     1.831     1.012     3.554     2.617:  34%|###4      | 33/96 [00:0\n",
      "       4/9     1.831     1.012     3.554     2.617:  35%|###5      | 34/96 [00:0\n",
      "       4/9     1.834     1.012     3.553      2.62:  35%|###5      | 34/96 [00:0\n",
      "       4/9     1.834     1.012     3.553      2.62:  36%|###6      | 35/96 [00:0\n",
      "       4/9     1.829     1.007     3.552     2.616:  36%|###6      | 35/96 [00:0\n",
      "       4/9     1.829     1.007     3.552     2.616:  38%|###7      | 36/96 [00:0\n",
      "       4/9     1.825     1.002     3.555     2.614:  38%|###7      | 36/96 [00:0\n",
      "       4/9     1.825     1.002     3.555     2.614:  39%|###8      | 37/96 [00:0\n",
      "       4/9     1.827     1.005     3.559     2.614:  39%|###8      | 37/96 [00:0\n",
      "       4/9     1.827     1.005     3.559     2.614:  40%|###9      | 38/96 [00:0\n",
      "       4/9     1.824     1.002     3.557      2.61:  40%|###9      | 38/96 [00:0\n",
      "       4/9     1.824     1.002     3.557      2.61:  41%|####      | 39/96 [00:0\n",
      "       4/9     1.834     1.003     3.561      2.61:  41%|####      | 39/96 [00:0\n",
      "       4/9     1.834     1.003     3.561      2.61:  42%|####1     | 40/96 [00:0\n",
      "       4/9     1.836     1.005     3.567     2.612:  42%|####1     | 40/96 [00:0\n",
      "       4/9     1.836     1.005     3.567     2.612:  43%|####2     | 41/96 [00:0\n",
      "       4/9     1.836         1     3.566     2.616:  43%|####2     | 41/96 [00:1\n",
      "       4/9     1.836         1     3.566     2.616:  44%|####3     | 42/96 [00:1\n",
      "       4/9     1.833     1.006     3.574     2.615:  44%|####3     | 42/96 [00:1\n",
      "       4/9     1.833     1.006     3.574     2.615:  45%|####4     | 43/96 [00:1\n",
      "       4/9     1.832     1.006     3.571     2.618:  45%|####4     | 43/96 [00:1\n",
      "       4/9     1.832     1.006     3.571     2.618:  46%|####5     | 44/96 [00:1\n",
      "       4/9     1.833     1.004     3.563     2.609:  46%|####5     | 44/96 [00:1\n",
      "       4/9     1.833     1.004     3.563     2.609:  47%|####6     | 45/96 [00:1\n",
      "       4/9     1.833     1.003     3.561     2.611:  47%|####6     | 45/96 [00:1\n",
      "       4/9     1.833     1.003     3.561     2.611:  48%|####7     | 46/96 [00:1\n",
      "       4/9     1.827         1     3.559     2.604:  48%|####7     | 46/96 [00:1\n",
      "       4/9     1.827         1     3.559     2.604:  49%|####8     | 47/96 [00:1\n",
      "       4/9     1.821    0.9991     3.557     2.603:  49%|####8     | 47/96 [00:1\n",
      "       4/9     1.821    0.9991     3.557     2.603:  50%|#####     | 48/96 [00:1\n",
      "       4/9     1.814    0.9986     3.558     2.601:  50%|#####     | 48/96 [00:1\n",
      "       4/9     1.814    0.9986     3.558     2.601:  51%|#####1    | 49/96 [00:1\n",
      "       4/9     1.816     0.996     3.556     2.602:  51%|#####1    | 49/96 [00:1\n",
      "       4/9     1.816     0.996     3.556     2.602:  52%|#####2    | 50/96 [00:1\n",
      "       4/9      1.82    0.9982     3.558     2.603:  52%|#####2    | 50/96 [00:1\n",
      "       4/9      1.82    0.9982     3.558     2.603:  53%|#####3    | 51/96 [00:1\n",
      "       4/9     1.819    0.9983     3.557     2.602:  53%|#####3    | 51/96 [00:1\n",
      "       4/9     1.819    0.9983     3.557     2.602:  54%|#####4    | 52/96 [00:1\n",
      "       4/9     1.811    0.9965     3.554     2.603:  54%|#####4    | 52/96 [00:1\n",
      "       4/9     1.811    0.9965     3.554     2.603:  55%|#####5    | 53/96 [00:1\n",
      "       4/9     1.809    0.9968     3.554     2.608:  55%|#####5    | 53/96 [00:1\n",
      "       4/9     1.809    0.9968     3.554     2.608:  56%|#####6    | 54/96 [00:1\n",
      "       4/9     1.807    0.9955     3.552     2.607:  56%|#####6    | 54/96 [00:1\n",
      "       4/9     1.807    0.9955     3.552     2.607:  57%|#####7    | 55/96 [00:1\n",
      "       4/9     1.804    0.9949      3.55     2.604:  57%|#####7    | 55/96 [00:1\n",
      "       4/9     1.804    0.9949      3.55     2.604:  58%|#####8    | 56/96 [00:1\n",
      "       4/9     1.802     0.995     3.546     2.605:  58%|#####8    | 56/96 [00:1\n",
      "       4/9     1.802     0.995     3.546     2.605:  59%|#####9    | 57/96 [00:1\n",
      "       4/9       1.8    0.9963     3.543     2.606:  59%|#####9    | 57/96 [00:1\n",
      "       4/9       1.8    0.9963     3.543     2.606:  60%|######    | 58/96 [00:1\n",
      "       4/9     1.796     0.995     3.536     2.601:  60%|######    | 58/96 [00:1\n",
      "       4/9     1.796     0.995     3.536     2.601:  61%|######1   | 59/96 [00:1\n",
      "       4/9     1.795    0.9937     3.536     2.602:  61%|######1   | 59/96 [00:1\n",
      "       4/9     1.795    0.9937     3.536     2.602:  62%|######2   | 60/96 [00:1\n",
      "       4/9     1.793     0.996     3.539     2.601:  62%|######2   | 60/96 [00:1\n",
      "       4/9     1.793     0.996     3.539     2.601:  64%|######3   | 61/96 [00:1\n",
      "       4/9      1.79     0.996     3.536     2.599:  64%|######3   | 61/96 [00:1\n",
      "       4/9      1.79     0.996     3.536     2.599:  65%|######4   | 62/96 [00:1\n",
      "       4/9     1.787    0.9946     3.532     2.596:  65%|######4   | 62/96 [00:1\n",
      "       4/9     1.787    0.9946     3.532     2.596:  66%|######5   | 63/96 [00:1\n",
      "       4/9     1.784     0.994     3.533     2.594:  66%|######5   | 63/96 [00:1\n",
      "       4/9     1.784     0.994     3.533     2.594:  67%|######6   | 64/96 [00:1\n",
      "       4/9     1.784    0.9964     3.537     2.588:  67%|######6   | 64/96 [00:1\n",
      "       4/9     1.784    0.9964     3.537     2.588:  68%|######7   | 65/96 [00:1\n",
      "       4/9     1.787    0.9945     3.536     2.584:  68%|######7   | 65/96 [00:1\n",
      "       4/9     1.787    0.9945     3.536     2.584:  69%|######8   | 66/96 [00:1\n",
      "       4/9     1.785    0.9943     3.541     2.587:  69%|######8   | 66/96 [00:1\n",
      "       4/9     1.785    0.9943     3.541     2.587:  70%|######9   | 67/96 [00:1\n",
      "       4/9     1.789    0.9977     3.542     2.588:  70%|######9   | 67/96 [00:1\n",
      "       4/9     1.789    0.9977     3.542     2.588:  71%|#######   | 68/96 [00:1\n",
      "       4/9     1.788    0.9974      3.54     2.586:  71%|#######   | 68/96 [00:1\n",
      "       4/9     1.788    0.9974      3.54     2.586:  72%|#######1  | 69/96 [00:1\n",
      "       4/9     1.789    0.9986     3.544     2.591:  72%|#######1  | 69/96 [00:1\n",
      "       4/9     1.789    0.9986     3.544     2.591:  73%|#######2  | 70/96 [00:1\n",
      "       4/9     1.785    0.9976     3.543      2.59:  73%|#######2  | 70/96 [00:1\n",
      "       4/9     1.785    0.9976     3.543      2.59:  74%|#######3  | 71/96 [00:1\n",
      "       4/9     1.787    0.9991     3.545     2.589:  74%|#######3  | 71/96 [00:1\n",
      "       4/9     1.787    0.9991     3.545     2.589:  75%|#######5  | 72/96 [00:1\n",
      "       4/9     1.791     1.001     3.548     2.591:  75%|#######5  | 72/96 [00:1\n",
      "       4/9     1.791     1.001     3.548     2.591:  76%|#######6  | 73/96 [00:1\n",
      "       4/9     1.792     1.002     3.549     2.589:  76%|#######6  | 73/96 [00:1\n",
      "       4/9     1.792     1.002     3.549     2.589:  77%|#######7  | 74/96 [00:1\n",
      "       4/9     1.793     1.003     3.549     2.592:  77%|#######7  | 74/96 [00:1\n",
      "       4/9     1.793     1.003     3.549     2.592:  78%|#######8  | 75/96 [00:1\n",
      "       4/9     1.791     1.002     3.546      2.59:  78%|#######8  | 75/96 [00:1\n",
      "       4/9     1.791     1.002     3.546      2.59:  79%|#######9  | 76/96 [00:1\n",
      "       4/9     1.792     1.002     3.544     2.586:  79%|#######9  | 76/96 [00:1\n",
      "       4/9     1.792     1.002     3.544     2.586:  80%|########  | 77/96 [00:1\n",
      "       4/9     1.792     1.002     3.542     2.586:  80%|########  | 77/96 [00:1\n",
      "       4/9     1.792     1.002     3.542     2.586:  81%|########1 | 78/96 [00:1\n",
      "       4/9     1.792         1     3.541     2.584:  81%|########1 | 78/96 [00:1\n",
      "       4/9     1.792         1     3.541     2.584:  82%|########2 | 79/96 [00:1\n",
      "       4/9     1.793         1     3.539     2.584:  82%|########2 | 79/96 [00:1\n",
      "       4/9     1.793         1     3.539     2.584:  83%|########3 | 80/96 [00:1\n",
      "       4/9     1.793     1.001     3.538     2.583:  83%|########3 | 80/96 [00:1\n",
      "       4/9     1.793     1.001     3.538     2.583:  84%|########4 | 81/96 [00:1\n",
      "       4/9     1.791    0.9994     3.539     2.579:  84%|########4 | 81/96 [00:1\n",
      "       4/9     1.791    0.9994     3.539     2.579:  85%|########5 | 82/96 [00:1\n",
      "       4/9      1.79     0.998     3.538     2.578:  85%|########5 | 82/96 [00:1\n",
      "       4/9      1.79     0.998     3.538     2.578:  86%|########6 | 83/96 [00:1\n",
      "       4/9     1.789    0.9967     3.535     2.578:  86%|########6 | 83/96 [00:2\n",
      "       4/9     1.789    0.9967     3.535     2.578:  88%|########7 | 84/96 [00:2\n",
      "       4/9     1.791    0.9961     3.535     2.578:  88%|########7 | 84/96 [00:2\n",
      "       4/9     1.791    0.9961     3.535     2.578:  89%|########8 | 85/96 [00:2\n",
      "       4/9     1.792    0.9976     3.536      2.58:  89%|########8 | 85/96 [00:2\n",
      "       4/9     1.792    0.9976     3.536      2.58:  90%|########9 | 86/96 [00:2\n",
      "       4/9     1.792    0.9967     3.535     2.583:  90%|########9 | 86/96 [00:2\n",
      "       4/9     1.792    0.9967     3.535     2.583:  91%|######### | 87/96 [00:2\n",
      "       4/9     1.794    0.9967     3.534     2.582:  91%|######### | 87/96 [00:2\n",
      "       4/9     1.794    0.9967     3.534     2.582:  92%|#########1| 88/96 [00:2\n",
      "       4/9      1.79    0.9948     3.531     2.582:  92%|#########1| 88/96 [00:2\n",
      "       4/9      1.79    0.9948     3.531     2.582:  93%|#########2| 89/96 [00:2\n",
      "       4/9      1.79    0.9925     3.531      2.58:  93%|#########2| 89/96 [00:2\n",
      "       4/9      1.79    0.9925     3.531      2.58:  94%|#########3| 90/96 [00:2\n",
      "       4/9     1.788    0.9914      3.53     2.581:  94%|#########3| 90/96 [00:2\n",
      "       4/9     1.788    0.9914      3.53     2.581:  95%|#########4| 91/96 [00:2\n",
      "       4/9     1.791    0.9933     3.534     2.585:  95%|#########4| 91/96 [00:2\n",
      "       4/9     1.791    0.9933     3.534     2.585:  96%|#########5| 92/96 [00:2\n",
      "       4/9      1.79    0.9927     3.531     2.584:  96%|#########5| 92/96 [00:2\n",
      "       4/9      1.79    0.9927     3.531     2.584:  97%|#########6| 93/96 [00:2\n",
      "       4/9     1.792    0.9944     3.531     2.585:  97%|#########6| 93/96 [00:2\n",
      "       4/9     1.792    0.9944     3.531     2.585:  98%|#########7| 94/96 [00:2\n",
      "       4/9     1.789     0.994     3.531     2.585:  98%|#########7| 94/96 [00:2\n",
      "       4/9     1.789     0.994     3.531     2.585:  99%|#########8| 95/96 [00:2\n",
      "       4/9     1.787    0.9952     3.531     2.585:  99%|#########8| 95/96 [00:2\n",
      "       4/9     1.787    0.9952     3.531     2.585: 100%|##########| 96/96 [00:2\n",
      "       4/9     1.787    0.9952     3.531     2.585: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:01<00:07,  1.09s/it]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:02<00:06,  1.03s/it]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:03<00:05,  1.10s/it]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:04<00:04,  1.07s/it]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:05<00:03,  1.03s/it]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:06<00:01,  1.01it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:07<00:01,  1.05s/it]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:07<00:00,  1.15it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:07<00:00,  1.03it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 4 | mAP@0.5: 0.1842492857803247 | mAP@0.50:0.95: 0.13946910220180445\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       5/9     1.861     1.116     3.295     2.592:   0%|          | 0/96 [00:00\n",
      "       5/9     1.861     1.116     3.295     2.592:   1%|1         | 1/96 [00:00\n",
      "       5/9     1.792     1.026     3.452     2.582:   1%|1         | 1/96 [00:00\n",
      "       5/9     1.792     1.026     3.452     2.582:   2%|2         | 2/96 [00:00\n",
      "       5/9     1.752     1.005     3.453     2.454:   2%|2         | 2/96 [00:00\n",
      "       5/9     1.752     1.005     3.453     2.454:   3%|3         | 3/96 [00:00\n",
      "       5/9     1.711    0.9645     3.455     2.431:   3%|3         | 3/96 [00:00\n",
      "       5/9     1.711    0.9645     3.455     2.431:   4%|4         | 4/96 [00:00\n",
      "       5/9     1.735    0.9764     3.468     2.458:   4%|4         | 4/96 [00:01\n",
      "       5/9     1.735    0.9764     3.468     2.458:   5%|5         | 5/96 [00:01\n",
      "       5/9     1.696    0.9888     3.493     2.455:   5%|5         | 5/96 [00:01\n",
      "       5/9     1.696    0.9888     3.493     2.455:   6%|6         | 6/96 [00:01\n",
      "       5/9     1.721    0.9922     3.456     2.467:   6%|6         | 6/96 [00:01\n",
      "       5/9     1.721    0.9922     3.456     2.467:   7%|7         | 7/96 [00:01\n",
      "       5/9     1.729    0.9784     3.427     2.449:   7%|7         | 7/96 [00:01\n",
      "       5/9     1.729    0.9784     3.427     2.449:   8%|8         | 8/96 [00:01\n",
      "       5/9     1.702    0.9648     3.425     2.427:   8%|8         | 8/96 [00:02\n",
      "       5/9     1.702    0.9648     3.425     2.427:   9%|9         | 9/96 [00:02\n",
      "       5/9     1.709    0.9676     3.424     2.429:   9%|9         | 9/96 [00:02\n",
      "       5/9     1.709    0.9676     3.424     2.429:  10%|#         | 10/96 [00:0\n",
      "       5/9     1.751    0.9703      3.45     2.435:  10%|#         | 10/96 [00:0\n",
      "       5/9     1.751    0.9703      3.45     2.435:  11%|#1        | 11/96 [00:0\n",
      "       5/9     1.766    0.9698     3.456     2.433:  11%|#1        | 11/96 [00:0\n",
      "       5/9     1.766    0.9698     3.456     2.433:  12%|#2        | 12/96 [00:0\n",
      "       5/9     1.791    0.9769     3.464     2.434:  12%|#2        | 12/96 [00:0\n",
      "       5/9     1.791    0.9769     3.464     2.434:  14%|#3        | 13/96 [00:0\n",
      "       5/9     1.791    0.9786     3.461     2.426:  14%|#3        | 13/96 [00:0\n",
      "       5/9     1.791    0.9786     3.461     2.426:  15%|#4        | 14/96 [00:0\n",
      "       5/9     1.798    0.9829     3.469     2.439:  15%|#4        | 14/96 [00:0\n",
      "       5/9     1.798    0.9829     3.469     2.439:  16%|#5        | 15/96 [00:0\n",
      "       5/9     1.794    0.9847     3.479     2.455:  16%|#5        | 15/96 [00:0\n",
      "       5/9     1.794    0.9847     3.479     2.455:  17%|#6        | 16/96 [00:0\n",
      "       5/9      1.78    0.9809     3.482     2.463:  17%|#6        | 16/96 [00:0\n",
      "       5/9      1.78    0.9809     3.482     2.463:  18%|#7        | 17/96 [00:0\n",
      "       5/9     1.775    0.9795     3.474     2.465:  18%|#7        | 17/96 [00:0\n",
      "       5/9     1.775    0.9795     3.474     2.465:  19%|#8        | 18/96 [00:0\n",
      "       5/9      1.78    0.9845     3.473     2.472:  19%|#8        | 18/96 [00:0\n",
      "       5/9      1.78    0.9845     3.473     2.472:  20%|#9        | 19/96 [00:0\n",
      "       5/9      1.77    0.9797     3.474      2.48:  20%|#9        | 19/96 [00:0\n",
      "       5/9      1.77    0.9797     3.474      2.48:  21%|##        | 20/96 [00:0\n",
      "       5/9     1.776    0.9768     3.463     2.477:  21%|##        | 20/96 [00:0\n",
      "       5/9     1.776    0.9768     3.463     2.477:  22%|##1       | 21/96 [00:0\n",
      "       5/9     1.768     0.974     3.458     2.471:  22%|##1       | 21/96 [00:0\n",
      "       5/9     1.768     0.974     3.458     2.471:  23%|##2       | 22/96 [00:0\n",
      "       5/9     1.774    0.9792     3.475      2.48:  23%|##2       | 22/96 [00:0\n",
      "       5/9     1.774    0.9792     3.475      2.48:  24%|##3       | 23/96 [00:0\n",
      "       5/9     1.775    0.9806     3.478     2.478:  24%|##3       | 23/96 [00:0\n",
      "       5/9     1.775    0.9806     3.478     2.478:  25%|##5       | 24/96 [00:0\n",
      "       5/9     1.775     0.975     3.464     2.469:  25%|##5       | 24/96 [00:0\n",
      "       5/9     1.775     0.975     3.464     2.469:  26%|##6       | 25/96 [00:0\n",
      "       5/9     1.766    0.9736     3.454     2.471:  26%|##6       | 25/96 [00:0\n",
      "       5/9     1.766    0.9736     3.454     2.471:  27%|##7       | 26/96 [00:0\n",
      "       5/9     1.785    0.9715     3.459     2.477:  27%|##7       | 26/96 [00:0\n",
      "       5/9     1.785    0.9715     3.459     2.477:  28%|##8       | 27/96 [00:0\n",
      "       5/9     1.789    0.9718     3.468     2.478:  28%|##8       | 27/96 [00:0\n",
      "       5/9     1.789    0.9718     3.468     2.478:  29%|##9       | 28/96 [00:0\n",
      "       5/9     1.791    0.9719     3.465     2.479:  29%|##9       | 28/96 [00:0\n",
      "       5/9     1.791    0.9719     3.465     2.479:  30%|###       | 29/96 [00:0\n",
      "       5/9      1.79    0.9735     3.471     2.477:  30%|###       | 29/96 [00:0\n",
      "       5/9      1.79    0.9735     3.471     2.477:  31%|###1      | 30/96 [00:0\n",
      "       5/9     1.797    0.9714     3.466     2.465:  31%|###1      | 30/96 [00:0\n",
      "       5/9     1.797    0.9714     3.466     2.465:  32%|###2      | 31/96 [00:0\n",
      "       5/9     1.802    0.9761      3.47     2.464:  32%|###2      | 31/96 [00:0\n",
      "       5/9     1.802    0.9761      3.47     2.464:  33%|###3      | 32/96 [00:0\n",
      "       5/9     1.808    0.9788      3.47     2.464:  33%|###3      | 32/96 [00:0\n",
      "       5/9     1.808    0.9788      3.47     2.464:  34%|###4      | 33/96 [00:0\n",
      "       5/9     1.802    0.9772     3.468      2.47:  34%|###4      | 33/96 [00:0\n",
      "       5/9     1.802    0.9772     3.468      2.47:  35%|###5      | 34/96 [00:0\n",
      "       5/9     1.802    0.9768     3.466     2.468:  35%|###5      | 34/96 [00:0\n",
      "       5/9     1.802    0.9768     3.466     2.468:  36%|###6      | 35/96 [00:0\n",
      "       5/9     1.799    0.9777     3.465     2.466:  36%|###6      | 35/96 [00:0\n",
      "       5/9     1.799    0.9777     3.465     2.466:  38%|###7      | 36/96 [00:0\n",
      "       5/9     1.796    0.9817     3.479     2.469:  38%|###7      | 36/96 [00:0\n",
      "       5/9     1.796    0.9817     3.479     2.469:  39%|###8      | 37/96 [00:0\n",
      "       5/9     1.791    0.9827     3.482     2.466:  39%|###8      | 37/96 [00:0\n",
      "       5/9     1.791    0.9827     3.482     2.466:  40%|###9      | 38/96 [00:0\n",
      "       5/9     1.789    0.9822     3.471     2.457:  40%|###9      | 38/96 [00:0\n",
      "       5/9     1.789    0.9822     3.471     2.457:  41%|####      | 39/96 [00:0\n",
      "       5/9     1.787    0.9815     3.468     2.449:  41%|####      | 39/96 [00:0\n",
      "       5/9     1.787    0.9815     3.468     2.449:  42%|####1     | 40/96 [00:0\n",
      "       5/9     1.788    0.9794     3.465      2.45:  42%|####1     | 40/96 [00:0\n",
      "       5/9     1.788    0.9794     3.465      2.45:  43%|####2     | 41/96 [00:0\n",
      "       5/9     1.788    0.9816     3.469     2.453:  43%|####2     | 41/96 [00:0\n",
      "       5/9     1.788    0.9816     3.469     2.453:  44%|####3     | 42/96 [00:0\n",
      "       5/9     1.793    0.9798     3.466     2.455:  44%|####3     | 42/96 [00:1\n",
      "       5/9     1.793    0.9798     3.466     2.455:  45%|####4     | 43/96 [00:1\n",
      "       5/9     1.793    0.9796     3.462      2.45:  45%|####4     | 43/96 [00:1\n",
      "       5/9     1.793    0.9796     3.462      2.45:  46%|####5     | 44/96 [00:1\n",
      "       5/9     1.787    0.9791     3.459      2.45:  46%|####5     | 44/96 [00:1\n",
      "       5/9     1.787    0.9791     3.459      2.45:  47%|####6     | 45/96 [00:1\n",
      "       5/9     1.784    0.9784     3.458     2.452:  47%|####6     | 45/96 [00:1\n",
      "       5/9     1.784    0.9784     3.458     2.452:  48%|####7     | 46/96 [00:1\n",
      "       5/9     1.776    0.9757     3.453     2.445:  48%|####7     | 46/96 [00:1\n",
      "       5/9     1.776    0.9757     3.453     2.445:  49%|####8     | 47/96 [00:1\n",
      "       5/9     1.777    0.9741     3.454     2.438:  49%|####8     | 47/96 [00:1\n",
      "       5/9     1.777    0.9741     3.454     2.438:  50%|#####     | 48/96 [00:1\n",
      "       5/9     1.774    0.9722     3.455     2.437:  50%|#####     | 48/96 [00:1\n",
      "       5/9     1.774    0.9722     3.455     2.437:  51%|#####1    | 49/96 [00:1\n",
      "       5/9     1.776    0.9736     3.457     2.435:  51%|#####1    | 49/96 [00:1\n",
      "       5/9     1.776    0.9736     3.457     2.435:  52%|#####2    | 50/96 [00:1\n",
      "       5/9     1.786    0.9763     3.462     2.438:  52%|#####2    | 50/96 [00:1\n",
      "       5/9     1.786    0.9763     3.462     2.438:  53%|#####3    | 51/96 [00:1\n",
      "       5/9     1.788    0.9776     3.462     2.432:  53%|#####3    | 51/96 [00:1\n",
      "       5/9     1.788    0.9776     3.462     2.432:  54%|#####4    | 52/96 [00:1\n",
      "       5/9     1.789    0.9768     3.461     2.433:  54%|#####4    | 52/96 [00:1\n",
      "       5/9     1.789    0.9768     3.461     2.433:  55%|#####5    | 53/96 [00:1\n",
      "       5/9     1.786    0.9798     3.467     2.433:  55%|#####5    | 53/96 [00:1\n",
      "       5/9     1.786    0.9798     3.467     2.433:  56%|#####6    | 54/96 [00:1\n",
      "       5/9      1.79    0.9821     3.469     2.431:  56%|#####6    | 54/96 [00:1\n",
      "       5/9      1.79    0.9821     3.469     2.431:  57%|#####7    | 55/96 [00:1\n",
      "       5/9     1.796    0.9866     3.472      2.43:  57%|#####7    | 55/96 [00:1\n",
      "       5/9     1.796    0.9866     3.472      2.43:  58%|#####8    | 56/96 [00:1\n",
      "       5/9     1.795    0.9859     3.472     2.435:  58%|#####8    | 56/96 [00:1\n",
      "       5/9     1.795    0.9859     3.472     2.435:  59%|#####9    | 57/96 [00:1\n",
      "       5/9       1.8     0.988     3.475     2.435:  59%|#####9    | 57/96 [00:1\n",
      "       5/9       1.8     0.988     3.475     2.435:  60%|######    | 58/96 [00:1\n",
      "       5/9     1.798    0.9874     3.474     2.432:  60%|######    | 58/96 [00:1\n",
      "       5/9     1.798    0.9874     3.474     2.432:  61%|######1   | 59/96 [00:1\n",
      "       5/9     1.803    0.9866     3.474     2.428:  61%|######1   | 59/96 [00:1\n",
      "       5/9     1.803    0.9866     3.474     2.428:  62%|######2   | 60/96 [00:1\n",
      "       5/9     1.802    0.9868     3.473     2.425:  62%|######2   | 60/96 [00:1\n",
      "       5/9     1.802    0.9868     3.473     2.425:  64%|######3   | 61/96 [00:1\n",
      "       5/9     1.803    0.9887     3.477     2.426:  64%|######3   | 61/96 [00:1\n",
      "       5/9     1.803    0.9887     3.477     2.426:  65%|######4   | 62/96 [00:1\n",
      "       5/9     1.802    0.9884     3.477     2.426:  65%|######4   | 62/96 [00:1\n",
      "       5/9     1.802    0.9884     3.477     2.426:  66%|######5   | 63/96 [00:1\n",
      "       5/9     1.803    0.9891     3.477     2.426:  66%|######5   | 63/96 [00:1\n",
      "       5/9     1.803    0.9891     3.477     2.426:  67%|######6   | 64/96 [00:1\n",
      "       5/9     1.808    0.9916     3.477     2.428:  67%|######6   | 64/96 [00:1\n",
      "       5/9     1.808    0.9916     3.477     2.428:  68%|######7   | 65/96 [00:1\n",
      "       5/9     1.805     0.991      3.48     2.431:  68%|######7   | 65/96 [00:1\n",
      "       5/9     1.805     0.991      3.48     2.431:  69%|######8   | 66/96 [00:1\n",
      "       5/9     1.808    0.9904     3.476      2.43:  69%|######8   | 66/96 [00:1\n",
      "       5/9     1.808    0.9904     3.476      2.43:  70%|######9   | 67/96 [00:1\n",
      "       5/9     1.802     0.987      3.47     2.432:  70%|######9   | 67/96 [00:1\n",
      "       5/9     1.802     0.987      3.47     2.432:  71%|#######   | 68/96 [00:1\n",
      "       5/9     1.803    0.9874     3.472     2.436:  71%|#######   | 68/96 [00:1\n",
      "       5/9     1.803    0.9874     3.472     2.436:  72%|#######1  | 69/96 [00:1\n",
      "       5/9     1.805    0.9871     3.471     2.438:  72%|#######1  | 69/96 [00:1\n",
      "       5/9     1.805    0.9871     3.471     2.438:  73%|#######2  | 70/96 [00:1\n",
      "       5/9     1.804    0.9857     3.468      2.44:  73%|#######2  | 70/96 [00:1\n",
      "       5/9     1.804    0.9857     3.468      2.44:  74%|#######3  | 71/96 [00:1\n",
      "       5/9     1.802    0.9867     3.468     2.441:  74%|#######3  | 71/96 [00:1\n",
      "       5/9     1.802    0.9867     3.468     2.441:  75%|#######5  | 72/96 [00:1\n",
      "       5/9     1.801    0.9856     3.468      2.44:  75%|#######5  | 72/96 [00:1\n",
      "       5/9     1.801    0.9856     3.468      2.44:  76%|#######6  | 73/96 [00:1\n",
      "       5/9       1.8    0.9866     3.467      2.44:  76%|#######6  | 73/96 [00:1\n",
      "       5/9       1.8    0.9866     3.467      2.44:  77%|#######7  | 74/96 [00:1\n",
      "       5/9       1.8    0.9893     3.471     2.444:  77%|#######7  | 74/96 [00:1\n",
      "       5/9       1.8    0.9893     3.471     2.444:  78%|#######8  | 75/96 [00:1\n",
      "       5/9     1.799     0.989     3.474     2.442:  78%|#######8  | 75/96 [00:1\n",
      "       5/9     1.799     0.989     3.474     2.442:  79%|#######9  | 76/96 [00:1\n",
      "       5/9     1.798    0.9896     3.473     2.441:  79%|#######9  | 76/96 [00:1\n",
      "       5/9     1.798    0.9896     3.473     2.441:  80%|########  | 77/96 [00:1\n",
      "       5/9     1.799    0.9883     3.469     2.439:  80%|########  | 77/96 [00:1\n",
      "       5/9     1.799    0.9883     3.469     2.439:  81%|########1 | 78/96 [00:1\n",
      "       5/9     1.798    0.9885     3.468     2.441:  81%|########1 | 78/96 [00:1\n",
      "       5/9     1.798    0.9885     3.468     2.441:  82%|########2 | 79/96 [00:1\n",
      "       5/9     1.798    0.9889     3.474     2.443:  82%|########2 | 79/96 [00:1\n",
      "       5/9     1.798    0.9889     3.474     2.443:  83%|########3 | 80/96 [00:1\n",
      "       5/9     1.796    0.9888     3.474     2.442:  83%|########3 | 80/96 [00:1\n",
      "       5/9     1.796    0.9888     3.474     2.442:  84%|########4 | 81/96 [00:1\n",
      "       5/9     1.797    0.9899     3.477      2.44:  84%|########4 | 81/96 [00:1\n",
      "       5/9     1.797    0.9899     3.477      2.44:  85%|########5 | 82/96 [00:1\n",
      "       5/9     1.797    0.9903     3.478     2.438:  85%|########5 | 82/96 [00:1\n",
      "       5/9     1.797    0.9903     3.478     2.438:  86%|########6 | 83/96 [00:1\n",
      "       5/9     1.797    0.9903     3.478      2.44:  86%|########6 | 83/96 [00:1\n",
      "       5/9     1.797    0.9903     3.478      2.44:  88%|########7 | 84/96 [00:1\n",
      "       5/9     1.799      0.99     3.478     2.441:  88%|########7 | 84/96 [00:2\n",
      "       5/9     1.799      0.99     3.478     2.441:  89%|########8 | 85/96 [00:2\n",
      "       5/9     1.805    0.9921     3.484     2.445:  89%|########8 | 85/96 [00:2\n",
      "       5/9     1.805    0.9921     3.484     2.445:  90%|########9 | 86/96 [00:2\n",
      "       5/9     1.804    0.9928     3.485     2.444:  90%|########9 | 86/96 [00:2\n",
      "       5/9     1.804    0.9928     3.485     2.444:  91%|######### | 87/96 [00:2\n",
      "       5/9     1.805    0.9929     3.483     2.446:  91%|######### | 87/96 [00:2\n",
      "       5/9     1.805    0.9929     3.483     2.446:  92%|#########1| 88/96 [00:2\n",
      "       5/9     1.805    0.9921     3.483     2.445:  92%|#########1| 88/96 [00:2\n",
      "       5/9     1.805    0.9921     3.483     2.445:  93%|#########2| 89/96 [00:2\n",
      "       5/9     1.804    0.9915     3.482     2.446:  93%|#########2| 89/96 [00:2\n",
      "       5/9     1.804    0.9915     3.482     2.446:  94%|#########3| 90/96 [00:2\n",
      "       5/9     1.804    0.9917     3.482     2.446:  94%|#########3| 90/96 [00:2\n",
      "       5/9     1.804    0.9917     3.482     2.446:  95%|#########4| 91/96 [00:2\n",
      "       5/9     1.801    0.9907     3.482     2.448:  95%|#########4| 91/96 [00:2\n",
      "       5/9     1.801    0.9907     3.482     2.448:  96%|#########5| 92/96 [00:2\n",
      "       5/9     1.803    0.9903     3.481     2.448:  96%|#########5| 92/96 [00:2\n",
      "       5/9     1.803    0.9903     3.481     2.448:  97%|#########6| 93/96 [00:2\n",
      "       5/9     1.803    0.9895     3.481      2.45:  97%|#########6| 93/96 [00:2\n",
      "       5/9     1.803    0.9895     3.481      2.45:  98%|#########7| 94/96 [00:2\n",
      "       5/9     1.805    0.9903     3.485     2.451:  98%|#########7| 94/96 [00:2\n",
      "       5/9     1.805    0.9903     3.485     2.451:  99%|#########8| 95/96 [00:2\n",
      "       5/9     1.804    0.9903     3.484     2.454:  99%|#########8| 95/96 [00:2\n",
      "       5/9     1.804    0.9903     3.484     2.454: 100%|##########| 96/96 [00:2\n",
      "       5/9     1.804    0.9903     3.484     2.454: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:00<00:06,  1.10it/s]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:04,  1.25it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.20it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.26it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:03<00:02,  1.29it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:04<00:01,  1.34it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:05<00:00,  1.28it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:05<00:00,  1.52it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:05<00:00,  1.35it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 5 | mAP@0.5: 0.17494717123798442 | mAP@0.50:0.95: 0.12789742782102728\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       6/9     1.727     1.072     3.597     2.353:   0%|          | 0/96 [00:00\n",
      "       6/9     1.727     1.072     3.597     2.353:   1%|1         | 1/96 [00:00\n",
      "       6/9     1.746     1.002     3.586     2.436:   1%|1         | 1/96 [00:00\n",
      "       6/9     1.746     1.002     3.586     2.436:   2%|2         | 2/96 [00:00\n",
      "       6/9     1.802     1.013     3.548      2.39:   2%|2         | 2/96 [00:00\n",
      "       6/9     1.802     1.013     3.548      2.39:   3%|3         | 3/96 [00:00\n",
      "       6/9     1.781    0.9835      3.46      2.34:   3%|3         | 3/96 [00:00\n",
      "       6/9     1.781    0.9835      3.46      2.34:   4%|4         | 4/96 [00:00\n",
      "       6/9     1.771    0.9582     3.492     2.366:   4%|4         | 4/96 [00:01\n",
      "       6/9     1.771    0.9582     3.492     2.366:   5%|5         | 5/96 [00:01\n",
      "       6/9     1.857    0.9674     3.501     2.352:   5%|5         | 5/96 [00:01\n",
      "       6/9     1.857    0.9674     3.501     2.352:   6%|6         | 6/96 [00:01\n",
      "       6/9     1.888    0.9635     3.487     2.327:   6%|6         | 6/96 [00:01\n",
      "       6/9     1.888    0.9635     3.487     2.327:   7%|7         | 7/96 [00:01\n",
      "       6/9      1.86     0.963     3.477     2.378:   7%|7         | 7/96 [00:01\n",
      "       6/9      1.86     0.963     3.477     2.378:   8%|8         | 8/96 [00:01\n",
      "       6/9     1.871    0.9606     3.471     2.406:   8%|8         | 8/96 [00:02\n",
      "       6/9     1.871    0.9606     3.471     2.406:   9%|9         | 9/96 [00:02\n",
      "       6/9     1.839    0.9541     3.476     2.401:   9%|9         | 9/96 [00:02\n",
      "       6/9     1.839    0.9541     3.476     2.401:  10%|#         | 10/96 [00:0\n",
      "       6/9     1.866    0.9626     3.484      2.41:  10%|#         | 10/96 [00:0\n",
      "       6/9     1.866    0.9626     3.484      2.41:  11%|#1        | 11/96 [00:0\n",
      "       6/9     1.858    0.9723     3.476     2.402:  11%|#1        | 11/96 [00:0\n",
      "       6/9     1.858    0.9723     3.476     2.402:  12%|#2        | 12/96 [00:0\n",
      "       6/9     1.856    0.9727     3.463     2.408:  12%|#2        | 12/96 [00:0\n",
      "       6/9     1.856    0.9727     3.463     2.408:  14%|#3        | 13/96 [00:0\n",
      "       6/9     1.871    0.9609     3.461     2.404:  14%|#3        | 13/96 [00:0\n",
      "       6/9     1.871    0.9609     3.461     2.404:  15%|#4        | 14/96 [00:0\n",
      "       6/9     1.878    0.9543     3.458       2.4:  15%|#4        | 14/96 [00:0\n",
      "       6/9     1.878    0.9543     3.458       2.4:  16%|#5        | 15/96 [00:0\n",
      "       6/9     1.877    0.9602     3.458     2.404:  16%|#5        | 15/96 [00:0\n",
      "       6/9     1.877    0.9602     3.458     2.404:  17%|#6        | 16/96 [00:0\n",
      "       6/9     1.869    0.9568     3.453     2.405:  17%|#6        | 16/96 [00:0\n",
      "       6/9     1.869    0.9568     3.453     2.405:  18%|#7        | 17/96 [00:0\n",
      "       6/9     1.875    0.9629     3.449     2.413:  18%|#7        | 17/96 [00:0\n",
      "       6/9     1.875    0.9629     3.449     2.413:  19%|#8        | 18/96 [00:0\n",
      "       6/9     1.846     0.951     3.436     2.415:  19%|#8        | 18/96 [00:0\n",
      "       6/9     1.846     0.951     3.436     2.415:  20%|#9        | 19/96 [00:0\n",
      "       6/9      1.84    0.9522     3.447      2.42:  20%|#9        | 19/96 [00:0\n",
      "       6/9      1.84    0.9522     3.447      2.42:  21%|##        | 20/96 [00:0\n",
      "       6/9     1.851    0.9488     3.431     2.396:  21%|##        | 20/96 [00:0\n",
      "       6/9     1.851    0.9488     3.431     2.396:  22%|##1       | 21/96 [00:0\n",
      "       6/9      1.86     0.946     3.432     2.401:  22%|##1       | 21/96 [00:0\n",
      "       6/9      1.86     0.946     3.432     2.401:  23%|##2       | 22/96 [00:0\n",
      "       6/9     1.856    0.9437     3.423     2.403:  23%|##2       | 22/96 [00:0\n",
      "       6/9     1.856    0.9437     3.423     2.403:  24%|##3       | 23/96 [00:0\n",
      "       6/9     1.859    0.9469     3.417     2.399:  24%|##3       | 23/96 [00:0\n",
      "       6/9     1.859    0.9469     3.417     2.399:  25%|##5       | 24/96 [00:0\n",
      "       6/9     1.855    0.9486     3.417     2.402:  25%|##5       | 24/96 [00:0\n",
      "       6/9     1.855    0.9486     3.417     2.402:  26%|##6       | 25/96 [00:0\n",
      "       6/9     1.843    0.9488     3.412     2.406:  26%|##6       | 25/96 [00:0\n",
      "       6/9     1.843    0.9488     3.412     2.406:  27%|##7       | 26/96 [00:0\n",
      "       6/9     1.852    0.9535     3.421     2.431:  27%|##7       | 26/96 [00:0\n",
      "       6/9     1.852    0.9535     3.421     2.431:  28%|##8       | 27/96 [00:0\n",
      "       6/9     1.842    0.9545     3.427     2.425:  28%|##8       | 27/96 [00:0\n",
      "       6/9     1.842    0.9545     3.427     2.425:  29%|##9       | 28/96 [00:0\n",
      "       6/9     1.847    0.9505     3.427     2.426:  29%|##9       | 28/96 [00:0\n",
      "       6/9     1.847    0.9505     3.427     2.426:  30%|###       | 29/96 [00:0\n",
      "       6/9     1.853    0.9528     3.432     2.427:  30%|###       | 29/96 [00:0\n",
      "       6/9     1.853    0.9528     3.432     2.427:  31%|###1      | 30/96 [00:0\n",
      "       6/9     1.867    0.9544     3.431     2.425:  31%|###1      | 30/96 [00:0\n",
      "       6/9     1.867    0.9544     3.431     2.425:  32%|###2      | 31/96 [00:0\n",
      "       6/9      1.85    0.9501     3.428     2.415:  32%|###2      | 31/96 [00:0\n",
      "       6/9      1.85    0.9501     3.428     2.415:  33%|###3      | 32/96 [00:0\n",
      "       6/9     1.843    0.9448     3.421     2.408:  33%|###3      | 32/96 [00:0\n",
      "       6/9     1.843    0.9448     3.421     2.408:  34%|###4      | 33/96 [00:0\n",
      "       6/9     1.845    0.9493     3.422     2.412:  34%|###4      | 33/96 [00:0\n",
      "       6/9     1.845    0.9493     3.422     2.412:  35%|###5      | 34/96 [00:0\n",
      "       6/9      1.85    0.9536     3.428     2.412:  35%|###5      | 34/96 [00:0\n",
      "       6/9      1.85    0.9536     3.428     2.412:  36%|###6      | 35/96 [00:0\n",
      "       6/9     1.843     0.952     3.426     2.404:  36%|###6      | 35/96 [00:0\n",
      "       6/9     1.843     0.952     3.426     2.404:  38%|###7      | 36/96 [00:0\n",
      "       6/9     1.838    0.9522     3.428       2.4:  38%|###7      | 36/96 [00:0\n",
      "       6/9     1.838    0.9522     3.428       2.4:  39%|###8      | 37/96 [00:0\n",
      "       6/9     1.827    0.9493     3.425     2.395:  39%|###8      | 37/96 [00:0\n",
      "       6/9     1.827    0.9493     3.425     2.395:  40%|###9      | 38/96 [00:0\n",
      "       6/9     1.821    0.9466     3.421     2.396:  40%|###9      | 38/96 [00:0\n",
      "       6/9     1.821    0.9466     3.421     2.396:  41%|####      | 39/96 [00:0\n",
      "       6/9     1.818    0.9476     3.425     2.398:  41%|####      | 39/96 [00:0\n",
      "       6/9     1.818    0.9476     3.425     2.398:  42%|####1     | 40/96 [00:0\n",
      "       6/9     1.821    0.9526     3.429     2.393:  42%|####1     | 40/96 [00:0\n",
      "       6/9     1.821    0.9526     3.429     2.393:  43%|####2     | 41/96 [00:0\n",
      "       6/9     1.822    0.9537     3.435     2.397:  43%|####2     | 41/96 [00:0\n",
      "       6/9     1.822    0.9537     3.435     2.397:  44%|####3     | 42/96 [00:0\n",
      "       6/9     1.825    0.9565     3.439     2.404:  44%|####3     | 42/96 [00:1\n",
      "       6/9     1.825    0.9565     3.439     2.404:  45%|####4     | 43/96 [00:1\n",
      "       6/9      1.84    0.9632     3.441     2.407:  45%|####4     | 43/96 [00:1\n",
      "       6/9      1.84    0.9632     3.441     2.407:  46%|####5     | 44/96 [00:1\n",
      "       6/9     1.842    0.9642     3.442     2.405:  46%|####5     | 44/96 [00:1\n",
      "       6/9     1.842    0.9642     3.442     2.405:  47%|####6     | 45/96 [00:1\n",
      "       6/9     1.843    0.9665     3.441     2.408:  47%|####6     | 45/96 [00:1\n",
      "       6/9     1.843    0.9665     3.441     2.408:  48%|####7     | 46/96 [00:1\n",
      "       6/9     1.841    0.9684     3.436     2.406:  48%|####7     | 46/96 [00:1\n",
      "       6/9     1.841    0.9684     3.436     2.406:  49%|####8     | 47/96 [00:1\n",
      "       6/9      1.84    0.9659     3.437     2.406:  49%|####8     | 47/96 [00:1\n",
      "       6/9      1.84    0.9659     3.437     2.406:  50%|#####     | 48/96 [00:1\n",
      "       6/9     1.836    0.9643     3.431       2.4:  50%|#####     | 48/96 [00:1\n",
      "       6/9     1.836    0.9643     3.431       2.4:  51%|#####1    | 49/96 [00:1\n",
      "       6/9     1.834    0.9647     3.431     2.397:  51%|#####1    | 49/96 [00:1\n",
      "       6/9     1.834    0.9647     3.431     2.397:  52%|#####2    | 50/96 [00:1\n",
      "       6/9     1.836    0.9687     3.437     2.394:  52%|#####2    | 50/96 [00:1\n",
      "       6/9     1.836    0.9687     3.437     2.394:  53%|#####3    | 51/96 [00:1\n",
      "       6/9     1.835    0.9708     3.439     2.395:  53%|#####3    | 51/96 [00:1\n",
      "       6/9     1.835    0.9708     3.439     2.395:  54%|#####4    | 52/96 [00:1\n",
      "       6/9     1.835    0.9712     3.437     2.395:  54%|#####4    | 52/96 [00:1\n",
      "       6/9     1.835    0.9712     3.437     2.395:  55%|#####5    | 53/96 [00:1\n",
      "       6/9     1.832    0.9729     3.438      2.39:  55%|#####5    | 53/96 [00:1\n",
      "       6/9     1.832    0.9729     3.438      2.39:  56%|#####6    | 54/96 [00:1\n",
      "       6/9     1.825    0.9712     3.437     2.392:  56%|#####6    | 54/96 [00:1\n",
      "       6/9     1.825    0.9712     3.437     2.392:  57%|#####7    | 55/96 [00:1\n",
      "       6/9      1.83     0.971     3.436     2.395:  57%|#####7    | 55/96 [00:1\n",
      "       6/9      1.83     0.971     3.436     2.395:  58%|#####8    | 56/96 [00:1\n",
      "       6/9     1.837    0.9752     3.439     2.391:  58%|#####8    | 56/96 [00:1\n",
      "       6/9     1.837    0.9752     3.439     2.391:  59%|#####9    | 57/96 [00:1\n",
      "       6/9     1.837    0.9798     3.444     2.389:  59%|#####9    | 57/96 [00:1\n",
      "       6/9     1.837    0.9798     3.444     2.389:  60%|######    | 58/96 [00:1\n",
      "       6/9     1.836    0.9804     3.443     2.386:  60%|######    | 58/96 [00:1\n",
      "       6/9     1.836    0.9804     3.443     2.386:  61%|######1   | 59/96 [00:1\n",
      "       6/9     1.828    0.9787     3.441     2.387:  61%|######1   | 59/96 [00:1\n",
      "       6/9     1.828    0.9787     3.441     2.387:  62%|######2   | 60/96 [00:1\n",
      "       6/9     1.827    0.9778      3.44     2.385:  62%|######2   | 60/96 [00:1\n",
      "       6/9     1.827    0.9778      3.44     2.385:  64%|######3   | 61/96 [00:1\n",
      "       6/9     1.827    0.9779     3.439     2.383:  64%|######3   | 61/96 [00:1\n",
      "       6/9     1.827    0.9779     3.439     2.383:  65%|######4   | 62/96 [00:1\n",
      "       6/9     1.825    0.9775     3.436     2.382:  65%|######4   | 62/96 [00:1\n",
      "       6/9     1.825    0.9775     3.436     2.382:  66%|######5   | 63/96 [00:1\n",
      "       6/9     1.825    0.9774     3.436     2.377:  66%|######5   | 63/96 [00:1\n",
      "       6/9     1.825    0.9774     3.436     2.377:  67%|######6   | 64/96 [00:1\n",
      "       6/9     1.824    0.9794     3.438     2.375:  67%|######6   | 64/96 [00:1\n",
      "       6/9     1.824    0.9794     3.438     2.375:  68%|######7   | 65/96 [00:1\n",
      "       6/9     1.822     0.978     3.434     2.376:  68%|######7   | 65/96 [00:1\n",
      "       6/9     1.822     0.978     3.434     2.376:  69%|######8   | 66/96 [00:1\n",
      "       6/9      1.82    0.9771     3.435     2.373:  69%|######8   | 66/96 [00:1\n",
      "       6/9      1.82    0.9771     3.435     2.373:  70%|######9   | 67/96 [00:1\n",
      "       6/9     1.818    0.9755     3.432     2.374:  70%|######9   | 67/96 [00:1\n",
      "       6/9     1.818    0.9755     3.432     2.374:  71%|#######   | 68/96 [00:1\n",
      "       6/9     1.819    0.9745     3.432     2.377:  71%|#######   | 68/96 [00:1\n",
      "       6/9     1.819    0.9745     3.432     2.377:  72%|#######1  | 69/96 [00:1\n",
      "       6/9      1.82    0.9729     3.436     2.378:  72%|#######1  | 69/96 [00:1\n",
      "       6/9      1.82    0.9729     3.436     2.378:  73%|#######2  | 70/96 [00:1\n",
      "       6/9     1.819    0.9721     3.431     2.377:  73%|#######2  | 70/96 [00:1\n",
      "       6/9     1.819    0.9721     3.431     2.377:  74%|#######3  | 71/96 [00:1\n",
      "       6/9      1.82    0.9701      3.43     2.377:  74%|#######3  | 71/96 [00:1\n",
      "       6/9      1.82    0.9701      3.43     2.377:  75%|#######5  | 72/96 [00:1\n",
      "       6/9      1.82    0.9703      3.43     2.378:  75%|#######5  | 72/96 [00:1\n",
      "       6/9      1.82    0.9703      3.43     2.378:  76%|#######6  | 73/96 [00:1\n",
      "       6/9     1.815     0.969     3.425     2.373:  76%|#######6  | 73/96 [00:1\n",
      "       6/9     1.815     0.969     3.425     2.373:  77%|#######7  | 74/96 [00:1\n",
      "       6/9     1.817    0.9709      3.43     2.373:  77%|#######7  | 74/96 [00:1\n",
      "       6/9     1.817    0.9709      3.43     2.373:  78%|#######8  | 75/96 [00:1\n",
      "       6/9     1.814    0.9726     3.432     2.375:  78%|#######8  | 75/96 [00:1\n",
      "       6/9     1.814    0.9726     3.432     2.375:  79%|#######9  | 76/96 [00:1\n",
      "       6/9     1.815    0.9751     3.433     2.378:  79%|#######9  | 76/96 [00:1\n",
      "       6/9     1.815    0.9751     3.433     2.378:  80%|########  | 77/96 [00:1\n",
      "       6/9     1.814    0.9743     3.432     2.382:  80%|########  | 77/96 [00:1\n",
      "       6/9     1.814    0.9743     3.432     2.382:  81%|########1 | 78/96 [00:1\n",
      "       6/9     1.815    0.9767     3.433      2.38:  81%|########1 | 78/96 [00:1\n",
      "       6/9     1.815    0.9767     3.433      2.38:  82%|########2 | 79/96 [00:1\n",
      "       6/9     1.816    0.9764     3.431     2.378:  82%|########2 | 79/96 [00:1\n",
      "       6/9     1.816    0.9764     3.431     2.378:  83%|########3 | 80/96 [00:1\n",
      "       6/9     1.818    0.9775     3.434     2.378:  83%|########3 | 80/96 [00:1\n",
      "       6/9     1.818    0.9775     3.434     2.378:  84%|########4 | 81/96 [00:1\n",
      "       6/9     1.816    0.9765     3.434     2.378:  84%|########4 | 81/96 [00:1\n",
      "       6/9     1.816    0.9765     3.434     2.378:  85%|########5 | 82/96 [00:1\n",
      "       6/9     1.812    0.9755     3.432     2.375:  85%|########5 | 82/96 [00:1\n",
      "       6/9     1.812    0.9755     3.432     2.375:  86%|########6 | 83/96 [00:1\n",
      "       6/9      1.81     0.974     3.433     2.378:  86%|########6 | 83/96 [00:1\n",
      "       6/9      1.81     0.974     3.433     2.378:  88%|########7 | 84/96 [00:1\n",
      "       6/9     1.809    0.9755     3.434     2.382:  88%|########7 | 84/96 [00:1\n",
      "       6/9     1.809    0.9755     3.434     2.382:  89%|########8 | 85/96 [00:1\n",
      "       6/9     1.805    0.9746     3.432     2.381:  89%|########8 | 85/96 [00:2\n",
      "       6/9     1.805    0.9746     3.432     2.381:  90%|########9 | 86/96 [00:2\n",
      "       6/9     1.806    0.9731      3.43     2.382:  90%|########9 | 86/96 [00:2\n",
      "       6/9     1.806    0.9731      3.43     2.382:  91%|######### | 87/96 [00:2\n",
      "       6/9     1.802    0.9733      3.43     2.381:  91%|######### | 87/96 [00:2\n",
      "       6/9     1.802    0.9733      3.43     2.381:  92%|#########1| 88/96 [00:2\n",
      "       6/9     1.801    0.9738     3.429      2.38:  92%|#########1| 88/96 [00:2\n",
      "       6/9     1.801    0.9738     3.429      2.38:  93%|#########2| 89/96 [00:2\n",
      "       6/9     1.802    0.9739     3.428     2.377:  93%|#########2| 89/96 [00:2\n",
      "       6/9     1.802    0.9739     3.428     2.377:  94%|#########3| 90/96 [00:2\n",
      "       6/9       1.8    0.9736     3.427     2.378:  94%|#########3| 90/96 [00:2\n",
      "       6/9       1.8    0.9736     3.427     2.378:  95%|#########4| 91/96 [00:2\n",
      "       6/9       1.8    0.9733     3.425     2.378:  95%|#########4| 91/96 [00:2\n",
      "       6/9       1.8    0.9733     3.425     2.378:  96%|#########5| 92/96 [00:2\n",
      "       6/9     1.802     0.973     3.425     2.374:  96%|#########5| 92/96 [00:2\n",
      "       6/9     1.802     0.973     3.425     2.374:  97%|#########6| 93/96 [00:2\n",
      "       6/9       1.8    0.9749     3.425     2.373:  97%|#########6| 93/96 [00:2\n",
      "       6/9       1.8    0.9749     3.425     2.373:  98%|#########7| 94/96 [00:2\n",
      "       6/9     1.803    0.9763     3.427     2.373:  98%|#########7| 94/96 [00:2\n",
      "       6/9     1.803    0.9763     3.427     2.373:  99%|#########8| 95/96 [00:2\n",
      "       6/9     1.805    0.9744     3.424      2.37:  99%|#########8| 95/96 [00:2\n",
      "       6/9     1.805    0.9744     3.424      2.37: 100%|##########| 96/96 [00:2\n",
      "       6/9     1.805    0.9744     3.424      2.37: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:00<00:06,  1.01it/s]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:05,  1.12it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.06it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.10it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:04<00:02,  1.15it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:05<00:01,  1.16it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:06<00:00,  1.04it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.23it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.15it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 6 | mAP@0.5: 0.20709063293294996 | mAP@0.50:0.95: 0.15421556596691724\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       7/9     1.441    0.8296     3.122     2.217:   0%|          | 0/96 [00:00\n",
      "       7/9     1.441    0.8296     3.122     2.217:   1%|1         | 1/96 [00:00\n",
      "       7/9      1.48     0.818     3.182     2.089:   1%|1         | 1/96 [00:00\n",
      "       7/9      1.48     0.818     3.182     2.089:   2%|2         | 2/96 [00:00\n",
      "       7/9     1.576     0.852     3.195     2.187:   2%|2         | 2/96 [00:00\n",
      "       7/9     1.576     0.852     3.195     2.187:   3%|3         | 3/96 [00:00\n",
      "       7/9     1.615    0.8599     3.225     2.224:   3%|3         | 3/96 [00:00\n",
      "       7/9     1.615    0.8599     3.225     2.224:   4%|4         | 4/96 [00:00\n",
      "       7/9     1.663    0.9101     3.288     2.216:   4%|4         | 4/96 [00:01\n",
      "       7/9     1.663    0.9101     3.288     2.216:   5%|5         | 5/96 [00:01\n",
      "       7/9     1.709    0.9336      3.35     2.256:   5%|5         | 5/96 [00:01\n",
      "       7/9     1.709    0.9336      3.35     2.256:   6%|6         | 6/96 [00:01\n",
      "       7/9     1.742     0.953     3.375     2.259:   6%|6         | 6/96 [00:01\n",
      "       7/9     1.742     0.953     3.375     2.259:   7%|7         | 7/96 [00:01\n",
      "       7/9     1.732    0.9752     3.409     2.298:   7%|7         | 7/96 [00:01\n",
      "       7/9     1.732    0.9752     3.409     2.298:   8%|8         | 8/96 [00:01\n",
      "       7/9     1.696    0.9663     3.386     2.293:   8%|8         | 8/96 [00:02\n",
      "       7/9     1.696    0.9663     3.386     2.293:   9%|9         | 9/96 [00:02\n",
      "       7/9     1.758    0.9712     3.437     2.299:   9%|9         | 9/96 [00:02\n",
      "       7/9     1.758    0.9712     3.437     2.299:  10%|#         | 10/96 [00:0\n",
      "       7/9     1.761    0.9651     3.414     2.298:  10%|#         | 10/96 [00:0\n",
      "       7/9     1.761    0.9651     3.414     2.298:  11%|#1        | 11/96 [00:0\n",
      "       7/9     1.749    0.9646     3.413     2.289:  11%|#1        | 11/96 [00:0\n",
      "       7/9     1.749    0.9646     3.413     2.289:  12%|#2        | 12/96 [00:0\n",
      "       7/9     1.744    0.9636     3.414     2.272:  12%|#2        | 12/96 [00:0\n",
      "       7/9     1.744    0.9636     3.414     2.272:  14%|#3        | 13/96 [00:0\n",
      "       7/9     1.748     0.951     3.415     2.273:  14%|#3        | 13/96 [00:0\n",
      "       7/9     1.748     0.951     3.415     2.273:  15%|#4        | 14/96 [00:0\n",
      "       7/9     1.774    0.9581     3.412     2.266:  15%|#4        | 14/96 [00:0\n",
      "       7/9     1.774    0.9581     3.412     2.266:  16%|#5        | 15/96 [00:0\n",
      "       7/9     1.746    0.9506     3.412     2.275:  16%|#5        | 15/96 [00:0\n",
      "       7/9     1.746    0.9506     3.412     2.275:  17%|#6        | 16/96 [00:0\n",
      "       7/9     1.733    0.9524     3.414     2.292:  17%|#6        | 16/96 [00:0\n",
      "       7/9     1.733    0.9524     3.414     2.292:  18%|#7        | 17/96 [00:0\n",
      "       7/9     1.738    0.9587     3.418     2.295:  18%|#7        | 17/96 [00:0\n",
      "       7/9     1.738    0.9587     3.418     2.295:  19%|#8        | 18/96 [00:0\n",
      "       7/9     1.731    0.9652     3.416     2.292:  19%|#8        | 18/96 [00:0\n",
      "       7/9     1.731    0.9652     3.416     2.292:  20%|#9        | 19/96 [00:0\n",
      "       7/9     1.732    0.9738     3.423     2.277:  20%|#9        | 19/96 [00:0\n",
      "       7/9     1.732    0.9738     3.423     2.277:  21%|##        | 20/96 [00:0\n",
      "       7/9      1.73    0.9725     3.426     2.277:  21%|##        | 20/96 [00:0\n",
      "       7/9      1.73    0.9725     3.426     2.277:  22%|##1       | 21/96 [00:0\n",
      "       7/9     1.734    0.9721     3.422     2.271:  22%|##1       | 21/96 [00:0\n",
      "       7/9     1.734    0.9721     3.422     2.271:  23%|##2       | 22/96 [00:0\n",
      "       7/9     1.744    0.9735      3.43     2.283:  23%|##2       | 22/96 [00:0\n",
      "       7/9     1.744    0.9735      3.43     2.283:  24%|##3       | 23/96 [00:0\n",
      "       7/9     1.747    0.9723     3.427     2.281:  24%|##3       | 23/96 [00:0\n",
      "       7/9     1.747    0.9723     3.427     2.281:  25%|##5       | 24/96 [00:0\n",
      "       7/9      1.75    0.9762     3.433      2.28:  25%|##5       | 24/96 [00:0\n",
      "       7/9      1.75    0.9762     3.433      2.28:  26%|##6       | 25/96 [00:0\n",
      "       7/9     1.746    0.9742     3.428     2.281:  26%|##6       | 25/96 [00:0\n",
      "       7/9     1.746    0.9742     3.428     2.281:  27%|##7       | 26/96 [00:0\n",
      "       7/9     1.755    0.9764     3.433     2.287:  27%|##7       | 26/96 [00:0\n",
      "       7/9     1.755    0.9764     3.433     2.287:  28%|##8       | 27/96 [00:0\n",
      "       7/9     1.749    0.9759     3.434     2.287:  28%|##8       | 27/96 [00:0\n",
      "       7/9     1.749    0.9759     3.434     2.287:  29%|##9       | 28/96 [00:0\n",
      "       7/9     1.747    0.9794      3.43     2.276:  29%|##9       | 28/96 [00:0\n",
      "       7/9     1.747    0.9794      3.43     2.276:  30%|###       | 29/96 [00:0\n",
      "       7/9     1.745    0.9751     3.431     2.279:  30%|###       | 29/96 [00:0\n",
      "       7/9     1.745    0.9751     3.431     2.279:  31%|###1      | 30/96 [00:0\n",
      "       7/9     1.741    0.9717     3.433     2.281:  31%|###1      | 30/96 [00:0\n",
      "       7/9     1.741    0.9717     3.433     2.281:  32%|###2      | 31/96 [00:0\n",
      "       7/9     1.746    0.9718     3.433     2.282:  32%|###2      | 31/96 [00:0\n",
      "       7/9     1.746    0.9718     3.433     2.282:  33%|###3      | 32/96 [00:0\n",
      "       7/9     1.741    0.9745     3.431     2.271:  33%|###3      | 32/96 [00:0\n",
      "       7/9     1.741    0.9745     3.431     2.271:  34%|###4      | 33/96 [00:0\n",
      "       7/9     1.751    0.9814     3.435     2.276:  34%|###4      | 33/96 [00:0\n",
      "       7/9     1.751    0.9814     3.435     2.276:  35%|###5      | 34/96 [00:0\n",
      "       7/9     1.751    0.9801      3.43     2.265:  35%|###5      | 34/96 [00:0\n",
      "       7/9     1.751    0.9801      3.43     2.265:  36%|###6      | 35/96 [00:0\n",
      "       7/9     1.748     0.978     3.427     2.263:  36%|###6      | 35/96 [00:0\n",
      "       7/9     1.748     0.978     3.427     2.263:  38%|###7      | 36/96 [00:0\n",
      "       7/9     1.748    0.9787     3.425     2.259:  38%|###7      | 36/96 [00:0\n",
      "       7/9     1.748    0.9787     3.425     2.259:  39%|###8      | 37/96 [00:0\n",
      "       7/9     1.744    0.9774      3.42     2.259:  39%|###8      | 37/96 [00:0\n",
      "       7/9     1.744    0.9774      3.42     2.259:  40%|###9      | 38/96 [00:0\n",
      "       7/9     1.738    0.9781     3.417     2.252:  40%|###9      | 38/96 [00:0\n",
      "       7/9     1.738    0.9781     3.417     2.252:  41%|####      | 39/96 [00:0\n",
      "       7/9      1.74    0.9763     3.414      2.25:  41%|####      | 39/96 [00:0\n",
      "       7/9      1.74    0.9763     3.414      2.25:  42%|####1     | 40/96 [00:0\n",
      "       7/9     1.733    0.9702     3.405     2.248:  42%|####1     | 40/96 [00:0\n",
      "       7/9     1.733    0.9702     3.405     2.248:  43%|####2     | 41/96 [00:0\n",
      "       7/9     1.734    0.9694     3.405     2.243:  43%|####2     | 41/96 [00:0\n",
      "       7/9     1.734    0.9694     3.405     2.243:  44%|####3     | 42/96 [00:0\n",
      "       7/9     1.733    0.9686     3.405     2.247:  44%|####3     | 42/96 [00:1\n",
      "       7/9     1.733    0.9686     3.405     2.247:  45%|####4     | 43/96 [00:1\n",
      "       7/9     1.732     0.972     3.407     2.247:  45%|####4     | 43/96 [00:1\n",
      "       7/9     1.732     0.972     3.407     2.247:  46%|####5     | 44/96 [00:1\n",
      "       7/9      1.73      0.97     3.411     2.249:  46%|####5     | 44/96 [00:1\n",
      "       7/9      1.73      0.97     3.411     2.249:  47%|####6     | 45/96 [00:1\n",
      "       7/9     1.735    0.9725     3.412      2.25:  47%|####6     | 45/96 [00:1\n",
      "       7/9     1.735    0.9725     3.412      2.25:  48%|####7     | 46/96 [00:1\n",
      "       7/9     1.735    0.9708     3.407      2.25:  48%|####7     | 46/96 [00:1\n",
      "       7/9     1.735    0.9708     3.407      2.25:  49%|####8     | 47/96 [00:1\n",
      "       7/9     1.739     0.975     3.409     2.248:  49%|####8     | 47/96 [00:1\n",
      "       7/9     1.739     0.975     3.409     2.248:  50%|#####     | 48/96 [00:1\n",
      "       7/9     1.744    0.9761     3.408      2.25:  50%|#####     | 48/96 [00:1\n",
      "       7/9     1.744    0.9761     3.408      2.25:  51%|#####1    | 49/96 [00:1\n",
      "       7/9     1.743    0.9754     3.409     2.249:  51%|#####1    | 49/96 [00:1\n",
      "       7/9     1.743    0.9754     3.409     2.249:  52%|#####2    | 50/96 [00:1\n",
      "       7/9     1.745    0.9761     3.413      2.25:  52%|#####2    | 50/96 [00:1\n",
      "       7/9     1.745    0.9761     3.413      2.25:  53%|#####3    | 51/96 [00:1\n",
      "       7/9     1.745    0.9746     3.413     2.255:  53%|#####3    | 51/96 [00:1\n",
      "       7/9     1.745    0.9746     3.413     2.255:  54%|#####4    | 52/96 [00:1\n",
      "       7/9     1.742    0.9757     3.412     2.253:  54%|#####4    | 52/96 [00:1\n",
      "       7/9     1.742    0.9757     3.412     2.253:  55%|#####5    | 53/96 [00:1\n",
      "       7/9     1.741    0.9766     3.416     2.255:  55%|#####5    | 53/96 [00:1\n",
      "       7/9     1.741    0.9766     3.416     2.255:  56%|#####6    | 54/96 [00:1\n",
      "       7/9     1.738    0.9764     3.412     2.255:  56%|#####6    | 54/96 [00:1\n",
      "       7/9     1.738    0.9764     3.412     2.255:  57%|#####7    | 55/96 [00:1\n",
      "       7/9     1.733    0.9735     3.408     2.255:  57%|#####7    | 55/96 [00:1\n",
      "       7/9     1.733    0.9735     3.408     2.255:  58%|#####8    | 56/96 [00:1\n",
      "       7/9     1.732    0.9746      3.41     2.254:  58%|#####8    | 56/96 [00:1\n",
      "       7/9     1.732    0.9746      3.41     2.254:  59%|#####9    | 57/96 [00:1\n",
      "       7/9     1.732    0.9738     3.409     2.252:  59%|#####9    | 57/96 [00:1\n",
      "       7/9     1.732    0.9738     3.409     2.252:  60%|######    | 58/96 [00:1\n",
      "       7/9     1.734    0.9729     3.406     2.256:  60%|######    | 58/96 [00:1\n",
      "       7/9     1.734    0.9729     3.406     2.256:  61%|######1   | 59/96 [00:1\n",
      "       7/9     1.736    0.9753     3.409     2.257:  61%|######1   | 59/96 [00:1\n",
      "       7/9     1.736    0.9753     3.409     2.257:  62%|######2   | 60/96 [00:1\n",
      "       7/9     1.737    0.9768     3.409     2.257:  62%|######2   | 60/96 [00:1\n",
      "       7/9     1.737    0.9768     3.409     2.257:  64%|######3   | 61/96 [00:1\n",
      "       7/9      1.74    0.9788     3.408     2.257:  64%|######3   | 61/96 [00:1\n",
      "       7/9      1.74    0.9788     3.408     2.257:  65%|######4   | 62/96 [00:1\n",
      "       7/9     1.742    0.9815      3.41     2.255:  65%|######4   | 62/96 [00:1\n",
      "       7/9     1.742    0.9815      3.41     2.255:  66%|######5   | 63/96 [00:1\n",
      "       7/9     1.743    0.9811     3.405     2.251:  66%|######5   | 63/96 [00:1\n",
      "       7/9     1.743    0.9811     3.405     2.251:  67%|######6   | 64/96 [00:1\n",
      "       7/9     1.744    0.9808     3.403     2.253:  67%|######6   | 64/96 [00:1\n",
      "       7/9     1.744    0.9808     3.403     2.253:  68%|######7   | 65/96 [00:1\n",
      "       7/9     1.741    0.9788     3.401     2.252:  68%|######7   | 65/96 [00:1\n",
      "       7/9     1.741    0.9788     3.401     2.252:  69%|######8   | 66/96 [00:1\n",
      "       7/9     1.749    0.9809     3.406     2.251:  69%|######8   | 66/96 [00:1\n",
      "       7/9     1.749    0.9809     3.406     2.251:  70%|######9   | 67/96 [00:1\n",
      "       7/9      1.75    0.9832     3.411     2.253:  70%|######9   | 67/96 [00:1\n",
      "       7/9      1.75    0.9832     3.411     2.253:  71%|#######   | 68/96 [00:1\n",
      "       7/9     1.751    0.9823     3.409     2.251:  71%|#######   | 68/96 [00:1\n",
      "       7/9     1.751    0.9823     3.409     2.251:  72%|#######1  | 69/96 [00:1\n",
      "       7/9     1.751    0.9833     3.408     2.251:  72%|#######1  | 69/96 [00:1\n",
      "       7/9     1.751    0.9833     3.408     2.251:  73%|#######2  | 70/96 [00:1\n",
      "       7/9     1.749    0.9829     3.409     2.252:  73%|#######2  | 70/96 [00:1\n",
      "       7/9     1.749    0.9829     3.409     2.252:  74%|#######3  | 71/96 [00:1\n",
      "       7/9     1.746    0.9801     3.407     2.255:  74%|#######3  | 71/96 [00:1\n",
      "       7/9     1.746    0.9801     3.407     2.255:  75%|#######5  | 72/96 [00:1\n",
      "       7/9     1.751      0.98     3.405      2.26:  75%|#######5  | 72/96 [00:1\n",
      "       7/9     1.751      0.98     3.405      2.26:  76%|#######6  | 73/96 [00:1\n",
      "       7/9      1.75    0.9833     3.408     2.263:  76%|#######6  | 73/96 [00:1\n",
      "       7/9      1.75    0.9833     3.408     2.263:  77%|#######7  | 74/96 [00:1\n",
      "       7/9      1.75    0.9833     3.406     2.261:  77%|#######7  | 74/96 [00:1\n",
      "       7/9      1.75    0.9833     3.406     2.261:  78%|#######8  | 75/96 [00:1\n",
      "       7/9      1.75     0.983     3.405     2.263:  78%|#######8  | 75/96 [00:1\n",
      "       7/9      1.75     0.983     3.405     2.263:  79%|#######9  | 76/96 [00:1\n",
      "       7/9     1.754    0.9834     3.409     2.266:  79%|#######9  | 76/96 [00:1\n",
      "       7/9     1.754    0.9834     3.409     2.266:  80%|########  | 77/96 [00:1\n",
      "       7/9     1.753    0.9815     3.405     2.266:  80%|########  | 77/96 [00:1\n",
      "       7/9     1.753    0.9815     3.405     2.266:  81%|########1 | 78/96 [00:1\n",
      "       7/9      1.76    0.9842     3.407     2.267:  81%|########1 | 78/96 [00:1\n",
      "       7/9      1.76    0.9842     3.407     2.267:  82%|########2 | 79/96 [00:1\n",
      "       7/9     1.757    0.9829     3.406     2.267:  82%|########2 | 79/96 [00:1\n",
      "       7/9     1.757    0.9829     3.406     2.267:  83%|########3 | 80/96 [00:1\n",
      "       7/9     1.759     0.981     3.406     2.264:  83%|########3 | 80/96 [00:1\n",
      "       7/9     1.759     0.981     3.406     2.264:  84%|########4 | 81/96 [00:1\n",
      "       7/9     1.757    0.9809     3.407     2.264:  84%|########4 | 81/96 [00:1\n",
      "       7/9     1.757    0.9809     3.407     2.264:  85%|########5 | 82/96 [00:1\n",
      "       7/9     1.759    0.9813     3.407     2.265:  85%|########5 | 82/96 [00:1\n",
      "       7/9     1.759    0.9813     3.407     2.265:  86%|########6 | 83/96 [00:1\n",
      "       7/9      1.76    0.9803     3.405     2.267:  86%|########6 | 83/96 [00:1\n",
      "       7/9      1.76    0.9803     3.405     2.267:  88%|########7 | 84/96 [00:1\n",
      "       7/9     1.761    0.9793     3.405      2.27:  88%|########7 | 84/96 [00:2\n",
      "       7/9     1.761    0.9793     3.405      2.27:  89%|########8 | 85/96 [00:2\n",
      "       7/9     1.764    0.9799     3.407      2.27:  89%|########8 | 85/96 [00:2\n",
      "       7/9     1.764    0.9799     3.407      2.27:  90%|########9 | 86/96 [00:2\n",
      "       7/9     1.763    0.9784     3.403     2.269:  90%|########9 | 86/96 [00:2\n",
      "       7/9     1.763    0.9784     3.403     2.269:  91%|######### | 87/96 [00:2\n",
      "       7/9     1.761    0.9775     3.402     2.269:  91%|######### | 87/96 [00:2\n",
      "       7/9     1.761    0.9775     3.402     2.269:  92%|#########1| 88/96 [00:2\n",
      "       7/9     1.761    0.9757     3.401     2.271:  92%|#########1| 88/96 [00:2\n",
      "       7/9     1.761    0.9757     3.401     2.271:  93%|#########2| 89/96 [00:2\n",
      "       7/9     1.761    0.9748       3.4     2.269:  93%|#########2| 89/96 [00:2\n",
      "       7/9     1.761    0.9748       3.4     2.269:  94%|#########3| 90/96 [00:2\n",
      "       7/9     1.762    0.9744       3.4     2.272:  94%|#########3| 90/96 [00:2\n",
      "       7/9     1.762    0.9744       3.4     2.272:  95%|#########4| 91/96 [00:2\n",
      "       7/9     1.761    0.9733     3.399     2.273:  95%|#########4| 91/96 [00:2\n",
      "       7/9     1.761    0.9733     3.399     2.273:  96%|#########5| 92/96 [00:2\n",
      "       7/9     1.769    0.9742     3.401     2.275:  96%|#########5| 92/96 [00:2\n",
      "       7/9     1.769    0.9742     3.401     2.275:  97%|#########6| 93/96 [00:2\n",
      "       7/9     1.771    0.9743     3.399     2.275:  97%|#########6| 93/96 [00:2\n",
      "       7/9     1.771    0.9743     3.399     2.275:  98%|#########7| 94/96 [00:2\n",
      "       7/9     1.771    0.9743     3.399     2.275:  98%|#########7| 94/96 [00:2\n",
      "       7/9     1.771    0.9743     3.399     2.275:  99%|#########8| 95/96 [00:2\n",
      "       7/9     1.766    0.9723     3.395     2.274:  99%|#########8| 95/96 [00:2\n",
      "       7/9     1.766    0.9723     3.395     2.274: 100%|##########| 96/96 [00:2\n",
      "       7/9     1.766    0.9723     3.395     2.274: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:01<00:07,  1.08s/it]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:02<00:05,  1.01it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.05it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.14it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:04<00:02,  1.23it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:05<00:01,  1.27it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:06<00:00,  1.19it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.42it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.24it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 7 | mAP@0.5: 0.24682541392316867 | mAP@0.50:0.95: 0.18544142714019893\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       8/9     1.728     1.042     3.315     2.215:   0%|          | 0/96 [00:00\n",
      "       8/9     1.728     1.042     3.315     2.215:   1%|1         | 1/96 [00:00\n",
      "       8/9     1.676    0.9735      3.31     2.469:   1%|1         | 1/96 [00:00\n",
      "       8/9     1.676    0.9735      3.31     2.469:   2%|2         | 2/96 [00:00\n",
      "       8/9     1.665    0.9385     3.305     2.476:   2%|2         | 2/96 [00:00\n",
      "       8/9     1.665    0.9385     3.305     2.476:   3%|3         | 3/96 [00:00\n",
      "       8/9     1.624    0.9372     3.294     2.406:   3%|3         | 3/96 [00:00\n",
      "       8/9     1.624    0.9372     3.294     2.406:   4%|4         | 4/96 [00:00\n",
      "       8/9     1.639    0.9492       3.3     2.334:   4%|4         | 4/96 [00:01\n",
      "       8/9     1.639    0.9492       3.3     2.334:   5%|5         | 5/96 [00:01\n",
      "       8/9     1.725      0.95     3.342     2.334:   5%|5         | 5/96 [00:01\n",
      "       8/9     1.725      0.95     3.342     2.334:   6%|6         | 6/96 [00:01\n",
      "       8/9      1.73    0.9635     3.343     2.336:   6%|6         | 6/96 [00:01\n",
      "       8/9      1.73    0.9635     3.343     2.336:   7%|7         | 7/96 [00:01\n",
      "       8/9     1.755    0.9755     3.352     2.337:   7%|7         | 7/96 [00:01\n",
      "       8/9     1.755    0.9755     3.352     2.337:   8%|8         | 8/96 [00:01\n",
      "       8/9     1.785    0.9825     3.377     2.344:   8%|8         | 8/96 [00:02\n",
      "       8/9     1.785    0.9825     3.377     2.344:   9%|9         | 9/96 [00:02\n",
      "       8/9     1.771    0.9762     3.376     2.326:   9%|9         | 9/96 [00:02\n",
      "       8/9     1.771    0.9762     3.376     2.326:  10%|#         | 10/96 [00:0\n",
      "       8/9     1.741    0.9579      3.35     2.306:  10%|#         | 10/96 [00:0\n",
      "       8/9     1.741    0.9579      3.35     2.306:  11%|#1        | 11/96 [00:0\n",
      "       8/9     1.751    0.9575     3.343     2.302:  11%|#1        | 11/96 [00:0\n",
      "       8/9     1.751    0.9575     3.343     2.302:  12%|#2        | 12/96 [00:0\n",
      "       8/9     1.771    0.9612     3.344     2.302:  12%|#2        | 12/96 [00:0\n",
      "       8/9     1.771    0.9612     3.344     2.302:  14%|#3        | 13/96 [00:0\n",
      "       8/9     1.786    0.9651      3.33     2.295:  14%|#3        | 13/96 [00:0\n",
      "       8/9     1.786    0.9651      3.33     2.295:  15%|#4        | 14/96 [00:0\n",
      "       8/9      1.79    0.9691     3.332      2.29:  15%|#4        | 14/96 [00:0\n",
      "       8/9      1.79    0.9691     3.332      2.29:  16%|#5        | 15/96 [00:0\n",
      "       8/9     1.772    0.9736     3.325     2.276:  16%|#5        | 15/96 [00:0\n",
      "       8/9     1.772    0.9736     3.325     2.276:  17%|#6        | 16/96 [00:0\n",
      "       8/9     1.771    0.9783     3.331     2.281:  17%|#6        | 16/96 [00:0\n",
      "       8/9     1.771    0.9783     3.331     2.281:  18%|#7        | 17/96 [00:0\n",
      "       8/9     1.773    0.9754     3.335     2.283:  18%|#7        | 17/96 [00:0\n",
      "       8/9     1.773    0.9754     3.335     2.283:  19%|#8        | 18/96 [00:0\n",
      "       8/9     1.756    0.9722     3.326     2.271:  19%|#8        | 18/96 [00:0\n",
      "       8/9     1.756    0.9722     3.326     2.271:  20%|#9        | 19/96 [00:0\n",
      "       8/9     1.752    0.9783     3.343      2.28:  20%|#9        | 19/96 [00:0\n",
      "       8/9     1.752    0.9783     3.343      2.28:  21%|##        | 20/96 [00:0\n",
      "       8/9     1.753    0.9791     3.349     2.281:  21%|##        | 20/96 [00:0\n",
      "       8/9     1.753    0.9791     3.349     2.281:  22%|##1       | 21/96 [00:0\n",
      "       8/9     1.754    0.9792     3.343     2.277:  22%|##1       | 21/96 [00:0\n",
      "       8/9     1.754    0.9792     3.343     2.277:  23%|##2       | 22/96 [00:0\n",
      "       8/9     1.751    0.9815     3.341     2.272:  23%|##2       | 22/96 [00:0\n",
      "       8/9     1.751    0.9815     3.341     2.272:  24%|##3       | 23/96 [00:0\n",
      "       8/9      1.76    0.9787      3.34      2.26:  24%|##3       | 23/96 [00:0\n",
      "       8/9      1.76    0.9787      3.34      2.26:  25%|##5       | 24/96 [00:0\n",
      "       8/9     1.762    0.9799     3.345     2.263:  25%|##5       | 24/96 [00:0\n",
      "       8/9     1.762    0.9799     3.345     2.263:  26%|##6       | 25/96 [00:0\n",
      "       8/9     1.763     0.979     3.342     2.254:  26%|##6       | 25/96 [00:0\n",
      "       8/9     1.763     0.979     3.342     2.254:  27%|##7       | 26/96 [00:0\n",
      "       8/9     1.773    0.9839     3.345     2.254:  27%|##7       | 26/96 [00:0\n",
      "       8/9     1.773    0.9839     3.345     2.254:  28%|##8       | 27/96 [00:0\n",
      "       8/9     1.774    0.9813     3.342     2.245:  28%|##8       | 27/96 [00:0\n",
      "       8/9     1.774    0.9813     3.342     2.245:  29%|##9       | 28/96 [00:0\n",
      "       8/9     1.768    0.9771      3.34     2.242:  29%|##9       | 28/96 [00:0\n",
      "       8/9     1.768    0.9771      3.34     2.242:  30%|###       | 29/96 [00:0\n",
      "       8/9     1.773    0.9806      3.34     2.239:  30%|###       | 29/96 [00:0\n",
      "       8/9     1.773    0.9806      3.34     2.239:  31%|###1      | 30/96 [00:0\n",
      "       8/9     1.766    0.9787     3.336     2.238:  31%|###1      | 30/96 [00:0\n",
      "       8/9     1.766    0.9787     3.336     2.238:  32%|###2      | 31/96 [00:0\n",
      "       8/9     1.772    0.9795     3.339     2.236:  32%|###2      | 31/96 [00:0\n",
      "       8/9     1.772    0.9795     3.339     2.236:  33%|###3      | 32/96 [00:0\n",
      "       8/9     1.759    0.9723     3.331     2.236:  33%|###3      | 32/96 [00:0\n",
      "       8/9     1.759    0.9723     3.331     2.236:  34%|###4      | 33/96 [00:0\n",
      "       8/9     1.763    0.9712     3.325     2.228:  34%|###4      | 33/96 [00:0\n",
      "       8/9     1.763    0.9712     3.325     2.228:  35%|###5      | 34/96 [00:0\n",
      "       8/9     1.763    0.9682     3.324     2.228:  35%|###5      | 34/96 [00:0\n",
      "       8/9     1.763    0.9682     3.324     2.228:  36%|###6      | 35/96 [00:0\n",
      "       8/9     1.761    0.9673     3.322     2.228:  36%|###6      | 35/96 [00:0\n",
      "       8/9     1.761    0.9673     3.322     2.228:  38%|###7      | 36/96 [00:0\n",
      "       8/9     1.757    0.9648     3.327     2.235:  38%|###7      | 36/96 [00:0\n",
      "       8/9     1.757    0.9648     3.327     2.235:  39%|###8      | 37/96 [00:0\n",
      "       8/9     1.752    0.9607      3.32     2.237:  39%|###8      | 37/96 [00:0\n",
      "       8/9     1.752    0.9607      3.32     2.237:  40%|###9      | 38/96 [00:0\n",
      "       8/9      1.75    0.9585     3.316     2.234:  40%|###9      | 38/96 [00:0\n",
      "       8/9      1.75    0.9585     3.316     2.234:  41%|####      | 39/96 [00:0\n",
      "       8/9     1.746    0.9558     3.308     2.237:  41%|####      | 39/96 [00:0\n",
      "       8/9     1.746    0.9558     3.308     2.237:  42%|####1     | 40/96 [00:0\n",
      "       8/9     1.744    0.9599     3.314     2.233:  42%|####1     | 40/96 [00:0\n",
      "       8/9     1.744    0.9599     3.314     2.233:  43%|####2     | 41/96 [00:0\n",
      "       8/9     1.739     0.957     3.313     2.234:  43%|####2     | 41/96 [00:0\n",
      "       8/9     1.739     0.957     3.313     2.234:  44%|####3     | 42/96 [00:0\n",
      "       8/9     1.735    0.9549      3.31     2.232:  44%|####3     | 42/96 [00:1\n",
      "       8/9     1.735    0.9549      3.31     2.232:  45%|####4     | 43/96 [00:1\n",
      "       8/9     1.728    0.9544     3.308     2.223:  45%|####4     | 43/96 [00:1\n",
      "       8/9     1.728    0.9544     3.308     2.223:  46%|####5     | 44/96 [00:1\n",
      "       8/9     1.729    0.9524     3.306     2.227:  46%|####5     | 44/96 [00:1\n",
      "       8/9     1.729    0.9524     3.306     2.227:  47%|####6     | 45/96 [00:1\n",
      "       8/9     1.735    0.9547     3.314      2.23:  47%|####6     | 45/96 [00:1\n",
      "       8/9     1.735    0.9547     3.314      2.23:  48%|####7     | 46/96 [00:1\n",
      "       8/9     1.742    0.9595      3.32     2.231:  48%|####7     | 46/96 [00:1\n",
      "       8/9     1.742    0.9595      3.32     2.231:  49%|####8     | 47/96 [00:1\n",
      "       8/9     1.748    0.9606     3.323      2.23:  49%|####8     | 47/96 [00:1\n",
      "       8/9     1.748    0.9606     3.323      2.23:  50%|#####     | 48/96 [00:1\n",
      "       8/9     1.751     0.962     3.323     2.229:  50%|#####     | 48/96 [00:1\n",
      "       8/9     1.751     0.962     3.323     2.229:  51%|#####1    | 49/96 [00:1\n",
      "       8/9     1.754    0.9626     3.321     2.224:  51%|#####1    | 49/96 [00:1\n",
      "       8/9     1.754    0.9626     3.321     2.224:  52%|#####2    | 50/96 [00:1\n",
      "       8/9     1.756    0.9649     3.322     2.223:  52%|#####2    | 50/96 [00:1\n",
      "       8/9     1.756    0.9649     3.322     2.223:  53%|#####3    | 51/96 [00:1\n",
      "       8/9     1.758    0.9669     3.324     2.224:  53%|#####3    | 51/96 [00:1\n",
      "       8/9     1.758    0.9669     3.324     2.224:  54%|#####4    | 52/96 [00:1\n",
      "       8/9     1.766    0.9686     3.327     2.224:  54%|#####4    | 52/96 [00:1\n",
      "       8/9     1.766    0.9686     3.327     2.224:  55%|#####5    | 53/96 [00:1\n",
      "       8/9      1.76    0.9655     3.319     2.226:  55%|#####5    | 53/96 [00:1\n",
      "       8/9      1.76    0.9655     3.319     2.226:  56%|#####6    | 54/96 [00:1\n",
      "       8/9      1.76    0.9633     3.312     2.224:  56%|#####6    | 54/96 [00:1\n",
      "       8/9      1.76    0.9633     3.312     2.224:  57%|#####7    | 55/96 [00:1\n",
      "       8/9     1.764    0.9658     3.316     2.224:  57%|#####7    | 55/96 [00:1\n",
      "       8/9     1.764    0.9658     3.316     2.224:  58%|#####8    | 56/96 [00:1\n",
      "       8/9     1.764    0.9643     3.315     2.223:  58%|#####8    | 56/96 [00:1\n",
      "       8/9     1.764    0.9643     3.315     2.223:  59%|#####9    | 57/96 [00:1\n",
      "       8/9     1.762    0.9644     3.315     2.228:  59%|#####9    | 57/96 [00:1\n",
      "       8/9     1.762    0.9644     3.315     2.228:  60%|######    | 58/96 [00:1\n",
      "       8/9     1.766    0.9637     3.315      2.23:  60%|######    | 58/96 [00:1\n",
      "       8/9     1.766    0.9637     3.315      2.23:  61%|######1   | 59/96 [00:1\n",
      "       8/9     1.764    0.9611      3.31     2.226:  61%|######1   | 59/96 [00:1\n",
      "       8/9     1.764    0.9611      3.31     2.226:  62%|######2   | 60/96 [00:1\n",
      "       8/9     1.761     0.962     3.308     2.218:  62%|######2   | 60/96 [00:1\n",
      "       8/9     1.761     0.962     3.308     2.218:  64%|######3   | 61/96 [00:1\n",
      "       8/9     1.756    0.9614     3.312     2.216:  64%|######3   | 61/96 [00:1\n",
      "       8/9     1.756    0.9614     3.312     2.216:  65%|######4   | 62/96 [00:1\n",
      "       8/9     1.757    0.9617     3.314     2.216:  65%|######4   | 62/96 [00:1\n",
      "       8/9     1.757    0.9617     3.314     2.216:  66%|######5   | 63/96 [00:1\n",
      "       8/9     1.755    0.9607     3.313     2.222:  66%|######5   | 63/96 [00:1\n",
      "       8/9     1.755    0.9607     3.313     2.222:  67%|######6   | 64/96 [00:1\n",
      "       8/9     1.755    0.9605     3.313     2.216:  67%|######6   | 64/96 [00:1\n",
      "       8/9     1.755    0.9605     3.313     2.216:  68%|######7   | 65/96 [00:1\n",
      "       8/9     1.757    0.9586     3.312     2.216:  68%|######7   | 65/96 [00:1\n",
      "       8/9     1.757    0.9586     3.312     2.216:  69%|######8   | 66/96 [00:1\n",
      "       8/9     1.764    0.9615     3.318     2.219:  69%|######8   | 66/96 [00:1\n",
      "       8/9     1.764    0.9615     3.318     2.219:  70%|######9   | 67/96 [00:1\n",
      "       8/9     1.762    0.9611     3.319     2.222:  70%|######9   | 67/96 [00:1\n",
      "       8/9     1.762    0.9611     3.319     2.222:  71%|#######   | 68/96 [00:1\n",
      "       8/9     1.761    0.9599     3.317     2.224:  71%|#######   | 68/96 [00:1\n",
      "       8/9     1.761    0.9599     3.317     2.224:  72%|#######1  | 69/96 [00:1\n",
      "       8/9     1.763    0.9608     3.322     2.224:  72%|#######1  | 69/96 [00:1\n",
      "       8/9     1.763    0.9608     3.322     2.224:  73%|#######2  | 70/96 [00:1\n",
      "       8/9     1.767    0.9617      3.32     2.219:  73%|#######2  | 70/96 [00:1\n",
      "       8/9     1.767    0.9617      3.32     2.219:  74%|#######3  | 71/96 [00:1\n",
      "       8/9     1.768    0.9621     3.318      2.22:  74%|#######3  | 71/96 [00:1\n",
      "       8/9     1.768    0.9621     3.318      2.22:  75%|#######5  | 72/96 [00:1\n",
      "       8/9     1.767    0.9605     3.315     2.217:  75%|#######5  | 72/96 [00:1\n",
      "       8/9     1.767    0.9605     3.315     2.217:  76%|#######6  | 73/96 [00:1\n",
      "       8/9     1.764    0.9592     3.313     2.218:  76%|#######6  | 73/96 [00:1\n",
      "       8/9     1.764    0.9592     3.313     2.218:  77%|#######7  | 74/96 [00:1\n",
      "       8/9     1.764    0.9591     3.313      2.22:  77%|#######7  | 74/96 [00:1\n",
      "       8/9     1.764    0.9591     3.313      2.22:  78%|#######8  | 75/96 [00:1\n",
      "       8/9     1.765    0.9578     3.311     2.214:  78%|#######8  | 75/96 [00:1\n",
      "       8/9     1.765    0.9578     3.311     2.214:  79%|#######9  | 76/96 [00:1\n",
      "       8/9     1.766    0.9584     3.315     2.215:  79%|#######9  | 76/96 [00:1\n",
      "       8/9     1.766    0.9584     3.315     2.215:  80%|########  | 77/96 [00:1\n",
      "       8/9     1.764    0.9595     3.317     2.216:  80%|########  | 77/96 [00:1\n",
      "       8/9     1.764    0.9595     3.317     2.216:  81%|########1 | 78/96 [00:1\n",
      "       8/9     1.764    0.9593     3.315     2.215:  81%|########1 | 78/96 [00:1\n",
      "       8/9     1.764    0.9593     3.315     2.215:  82%|########2 | 79/96 [00:1\n",
      "       8/9     1.769    0.9623      3.32     2.214:  82%|########2 | 79/96 [00:1\n",
      "       8/9     1.769    0.9623      3.32     2.214:  83%|########3 | 80/96 [00:1\n",
      "       8/9     1.769    0.9616     3.323     2.217:  83%|########3 | 80/96 [00:1\n",
      "       8/9     1.769    0.9616     3.323     2.217:  84%|########4 | 81/96 [00:1\n",
      "       8/9     1.769    0.9601     3.321      2.22:  84%|########4 | 81/96 [00:1\n",
      "       8/9     1.769    0.9601     3.321      2.22:  85%|########5 | 82/96 [00:1\n",
      "       8/9      1.77    0.9605     3.323      2.22:  85%|########5 | 82/96 [00:1\n",
      "       8/9      1.77    0.9605     3.323      2.22:  86%|########6 | 83/96 [00:1\n",
      "       8/9     1.768    0.9597      3.32     2.221:  86%|########6 | 83/96 [00:1\n",
      "       8/9     1.768    0.9597      3.32     2.221:  88%|########7 | 84/96 [00:1\n",
      "       8/9     1.768    0.9613     3.323     2.223:  88%|########7 | 84/96 [00:2\n",
      "       8/9     1.768    0.9613     3.323     2.223:  89%|########8 | 85/96 [00:2\n",
      "       8/9     1.766    0.9604     3.325     2.225:  89%|########8 | 85/96 [00:2\n",
      "       8/9     1.766    0.9604     3.325     2.225:  90%|########9 | 86/96 [00:2\n",
      "       8/9     1.764    0.9587     3.324     2.223:  90%|########9 | 86/96 [00:2\n",
      "       8/9     1.764    0.9587     3.324     2.223:  91%|######### | 87/96 [00:2\n",
      "       8/9     1.765    0.9588     3.324     2.221:  91%|######### | 87/96 [00:2\n",
      "       8/9     1.765    0.9588     3.324     2.221:  92%|#########1| 88/96 [00:2\n",
      "       8/9     1.767    0.9589     3.324     2.219:  92%|#########1| 88/96 [00:2\n",
      "       8/9     1.767    0.9589     3.324     2.219:  93%|#########2| 89/96 [00:2\n",
      "       8/9     1.772      0.96     3.325     2.223:  93%|#########2| 89/96 [00:2\n",
      "       8/9     1.772      0.96     3.325     2.223:  94%|#########3| 90/96 [00:2\n",
      "       8/9     1.773    0.9613     3.331     2.224:  94%|#########3| 90/96 [00:2\n",
      "       8/9     1.773    0.9613     3.331     2.224:  95%|#########4| 91/96 [00:2\n",
      "       8/9      1.77    0.9613      3.33     2.222:  95%|#########4| 91/96 [00:2\n",
      "       8/9      1.77    0.9613      3.33     2.222:  96%|#########5| 92/96 [00:2\n",
      "       8/9     1.766    0.9597     3.328     2.223:  96%|#########5| 92/96 [00:2\n",
      "       8/9     1.766    0.9597     3.328     2.223:  97%|#########6| 93/96 [00:2\n",
      "       8/9     1.765    0.9597     3.327     2.224:  97%|#########6| 93/96 [00:2\n",
      "       8/9     1.765    0.9597     3.327     2.224:  98%|#########7| 94/96 [00:2\n",
      "       8/9      1.77    0.9602     3.332     2.225:  98%|#########7| 94/96 [00:2\n",
      "       8/9      1.77    0.9602     3.332     2.225:  99%|#########8| 95/96 [00:2\n",
      "       8/9     1.775    0.9591     3.331     2.227:  99%|#########8| 95/96 [00:2\n",
      "       8/9     1.775    0.9591     3.331     2.227: 100%|##########| 96/96 [00:2\n",
      "       8/9     1.775    0.9591     3.331     2.227: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:00<00:05,  1.30it/s]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:04,  1.39it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:03,  1.37it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:02<00:02,  1.44it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:03<00:01,  1.52it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:04<00:01,  1.54it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:04<00:00,  1.46it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:05<00:00,  1.75it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:05<00:00,  1.55it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 8 | mAP@0.5: 0.2792292430479404 | mAP@0.50:0.95: 0.21403850143520775\n",
      "\n",
      "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
      "\n",
      "  0%|          | 0/96 [00:00<?, ?it/s]                                          \n",
      "       9/9     2.014     1.072     3.436     2.003:   0%|          | 0/96 [00:00\n",
      "       9/9     2.014     1.072     3.436     2.003:   1%|1         | 1/96 [00:00\n",
      "       9/9     2.083    0.9984     3.338     2.167:   1%|1         | 1/96 [00:00\n",
      "       9/9     2.083    0.9984     3.338     2.167:   2%|2         | 2/96 [00:00\n",
      "       9/9     2.089     0.963     3.374      2.24:   2%|2         | 2/96 [00:00\n",
      "       9/9     2.089     0.963     3.374      2.24:   3%|3         | 3/96 [00:00\n",
      "       9/9     2.034    0.9963     3.368     2.245:   3%|3         | 3/96 [00:00\n",
      "       9/9     2.034    0.9963     3.368     2.245:   4%|4         | 4/96 [00:00\n",
      "       9/9     1.931    0.9584     3.338     2.223:   4%|4         | 4/96 [00:01\n",
      "       9/9     1.931    0.9584     3.338     2.223:   5%|5         | 5/96 [00:01\n",
      "       9/9     1.917    0.9539      3.35      2.24:   5%|5         | 5/96 [00:01\n",
      "       9/9     1.917    0.9539      3.35      2.24:   6%|6         | 6/96 [00:01\n",
      "       9/9     1.919    0.9702      3.37     2.284:   6%|6         | 6/96 [00:01\n",
      "       9/9     1.919    0.9702      3.37     2.284:   7%|7         | 7/96 [00:01\n",
      "       9/9     1.912    0.9777     3.377     2.246:   7%|7         | 7/96 [00:01\n",
      "       9/9     1.912    0.9777     3.377     2.246:   8%|8         | 8/96 [00:01\n",
      "       9/9     1.904     0.962     3.378      2.24:   8%|8         | 8/96 [00:02\n",
      "       9/9     1.904     0.962     3.378      2.24:   9%|9         | 9/96 [00:02\n",
      "       9/9      1.89    0.9784     3.401     2.234:   9%|9         | 9/96 [00:02\n",
      "       9/9      1.89    0.9784     3.401     2.234:  10%|#         | 10/96 [00:0\n",
      "       9/9     1.886    0.9883     3.407     2.218:  10%|#         | 10/96 [00:0\n",
      "       9/9     1.886    0.9883     3.407     2.218:  11%|#1        | 11/96 [00:0\n",
      "       9/9     1.864    0.9813     3.397     2.231:  11%|#1        | 11/96 [00:0\n",
      "       9/9     1.864    0.9813     3.397     2.231:  12%|#2        | 12/96 [00:0\n",
      "       9/9     1.857    0.9812     3.393     2.206:  12%|#2        | 12/96 [00:0\n",
      "       9/9     1.857    0.9812     3.393     2.206:  14%|#3        | 13/96 [00:0\n",
      "       9/9     1.836    0.9747     3.389     2.203:  14%|#3        | 13/96 [00:0\n",
      "       9/9     1.836    0.9747     3.389     2.203:  15%|#4        | 14/96 [00:0\n",
      "       9/9     1.824    0.9604     3.359     2.174:  15%|#4        | 14/96 [00:0\n",
      "       9/9     1.824    0.9604     3.359     2.174:  16%|#5        | 15/96 [00:0\n",
      "       9/9     1.812    0.9575     3.354     2.186:  16%|#5        | 15/96 [00:0\n",
      "       9/9     1.812    0.9575     3.354     2.186:  17%|#6        | 16/96 [00:0\n",
      "       9/9     1.793     0.948     3.344     2.192:  17%|#6        | 16/96 [00:0\n",
      "       9/9     1.793     0.948     3.344     2.192:  18%|#7        | 17/96 [00:0\n",
      "       9/9     1.782    0.9429     3.328     2.204:  18%|#7        | 17/96 [00:0\n",
      "       9/9     1.782    0.9429     3.328     2.204:  19%|#8        | 18/96 [00:0\n",
      "       9/9     1.802    0.9401      3.34     2.206:  19%|#8        | 18/96 [00:0\n",
      "       9/9     1.802    0.9401      3.34     2.206:  20%|#9        | 19/96 [00:0\n",
      "       9/9     1.816    0.9495     3.372     2.213:  20%|#9        | 19/96 [00:0\n",
      "       9/9     1.816    0.9495     3.372     2.213:  21%|##        | 20/96 [00:0\n",
      "       9/9     1.823     0.946     3.368     2.224:  21%|##        | 20/96 [00:0\n",
      "       9/9     1.823     0.946     3.368     2.224:  22%|##1       | 21/96 [00:0\n",
      "       9/9     1.817    0.9473      3.37     2.216:  22%|##1       | 21/96 [00:0\n",
      "       9/9     1.817    0.9473      3.37     2.216:  23%|##2       | 22/96 [00:0\n",
      "       9/9      1.81    0.9472     3.372     2.204:  23%|##2       | 22/96 [00:0\n",
      "       9/9      1.81    0.9472     3.372     2.204:  24%|##3       | 23/96 [00:0\n",
      "       9/9     1.799    0.9401     3.362     2.202:  24%|##3       | 23/96 [00:0\n",
      "       9/9     1.799    0.9401     3.362     2.202:  25%|##5       | 24/96 [00:0\n",
      "       9/9     1.786    0.9379     3.358     2.203:  25%|##5       | 24/96 [00:0\n",
      "       9/9     1.786    0.9379     3.358     2.203:  26%|##6       | 25/96 [00:0\n",
      "       9/9     1.795    0.9382     3.356     2.195:  26%|##6       | 25/96 [00:0\n",
      "       9/9     1.795    0.9382     3.356     2.195:  27%|##7       | 26/96 [00:0\n",
      "       9/9     1.788    0.9402      3.36     2.202:  27%|##7       | 26/96 [00:0\n",
      "       9/9     1.788    0.9402      3.36     2.202:  28%|##8       | 27/96 [00:0\n",
      "       9/9     1.791    0.9388     3.349     2.199:  28%|##8       | 27/96 [00:0\n",
      "       9/9     1.791    0.9388     3.349     2.199:  29%|##9       | 28/96 [00:0\n",
      "       9/9     1.782    0.9355     3.341     2.197:  29%|##9       | 28/96 [00:0\n",
      "       9/9     1.782    0.9355     3.341     2.197:  30%|###       | 29/96 [00:0\n",
      "       9/9     1.789    0.9344     3.342     2.198:  30%|###       | 29/96 [00:0\n",
      "       9/9     1.789    0.9344     3.342     2.198:  31%|###1      | 30/96 [00:0\n",
      "       9/9     1.792    0.9383     3.347     2.209:  31%|###1      | 30/96 [00:0\n",
      "       9/9     1.792    0.9383     3.347     2.209:  32%|###2      | 31/96 [00:0\n",
      "       9/9     1.792    0.9383     3.341     2.203:  32%|###2      | 31/96 [00:0\n",
      "       9/9     1.792    0.9383     3.341     2.203:  33%|###3      | 32/96 [00:0\n",
      "       9/9     1.789    0.9394     3.341     2.208:  33%|###3      | 32/96 [00:0\n",
      "       9/9     1.789    0.9394     3.341     2.208:  34%|###4      | 33/96 [00:0\n",
      "       9/9     1.804    0.9477     3.354     2.212:  34%|###4      | 33/96 [00:0\n",
      "       9/9     1.804    0.9477     3.354     2.212:  35%|###5      | 34/96 [00:0\n",
      "       9/9     1.811    0.9489     3.356     2.209:  35%|###5      | 34/96 [00:0\n",
      "       9/9     1.811    0.9489     3.356     2.209:  36%|###6      | 35/96 [00:0\n",
      "       9/9     1.803    0.9481     3.358     2.204:  36%|###6      | 35/96 [00:0\n",
      "       9/9     1.803    0.9481     3.358     2.204:  38%|###7      | 36/96 [00:0\n",
      "       9/9       1.8    0.9487     3.355     2.197:  38%|###7      | 36/96 [00:0\n",
      "       9/9       1.8    0.9487     3.355     2.197:  39%|###8      | 37/96 [00:0\n",
      "       9/9     1.799    0.9495     3.351     2.192:  39%|###8      | 37/96 [00:0\n",
      "       9/9     1.799    0.9495     3.351     2.192:  40%|###9      | 38/96 [00:0\n",
      "       9/9     1.797    0.9475     3.343     2.184:  40%|###9      | 38/96 [00:0\n",
      "       9/9     1.797    0.9475     3.343     2.184:  41%|####      | 39/96 [00:0\n",
      "       9/9     1.807    0.9581     3.348     2.186:  41%|####      | 39/96 [00:0\n",
      "       9/9     1.807    0.9581     3.348     2.186:  42%|####1     | 40/96 [00:0\n",
      "       9/9     1.802    0.9542      3.34     2.196:  42%|####1     | 40/96 [00:0\n",
      "       9/9     1.802    0.9542      3.34     2.196:  43%|####2     | 41/96 [00:0\n",
      "       9/9     1.797    0.9542     3.341     2.195:  43%|####2     | 41/96 [00:0\n",
      "       9/9     1.797    0.9542     3.341     2.195:  44%|####3     | 42/96 [00:0\n",
      "       9/9     1.792    0.9547      3.34     2.197:  44%|####3     | 42/96 [00:1\n",
      "       9/9     1.792    0.9547      3.34     2.197:  45%|####4     | 43/96 [00:1\n",
      "       9/9     1.792    0.9566     3.338     2.201:  45%|####4     | 43/96 [00:1\n",
      "       9/9     1.792    0.9566     3.338     2.201:  46%|####5     | 44/96 [00:1\n",
      "       9/9     1.801    0.9565     3.338     2.198:  46%|####5     | 44/96 [00:1\n",
      "       9/9     1.801    0.9565     3.338     2.198:  47%|####6     | 45/96 [00:1\n",
      "       9/9     1.809    0.9565     3.335     2.208:  47%|####6     | 45/96 [00:1\n",
      "       9/9     1.809    0.9565     3.335     2.208:  48%|####7     | 46/96 [00:1\n",
      "       9/9     1.808    0.9549     3.334     2.209:  48%|####7     | 46/96 [00:1\n",
      "       9/9     1.808    0.9549     3.334     2.209:  49%|####8     | 47/96 [00:1\n",
      "       9/9     1.813     0.959     3.338     2.206:  49%|####8     | 47/96 [00:1\n",
      "       9/9     1.813     0.959     3.338     2.206:  50%|#####     | 48/96 [00:1\n",
      "       9/9     1.811    0.9587     3.344     2.206:  50%|#####     | 48/96 [00:1\n",
      "       9/9     1.811    0.9587     3.344     2.206:  51%|#####1    | 49/96 [00:1\n",
      "       9/9     1.809    0.9561     3.344     2.203:  51%|#####1    | 49/96 [00:1\n",
      "       9/9     1.809    0.9561     3.344     2.203:  52%|#####2    | 50/96 [00:1\n",
      "       9/9     1.803    0.9525     3.337     2.197:  52%|#####2    | 50/96 [00:1\n",
      "       9/9     1.803    0.9525     3.337     2.197:  53%|#####3    | 51/96 [00:1\n",
      "       9/9     1.799    0.9535     3.337     2.199:  53%|#####3    | 51/96 [00:1\n",
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      "       9/9     1.799    0.9564     3.337     2.195:  54%|#####4    | 52/96 [00:1\n",
      "       9/9     1.799    0.9564     3.337     2.195:  55%|#####5    | 53/96 [00:1\n",
      "       9/9     1.796    0.9583     3.335     2.197:  55%|#####5    | 53/96 [00:1\n",
      "       9/9     1.796    0.9583     3.335     2.197:  56%|#####6    | 54/96 [00:1\n",
      "       9/9     1.799    0.9595     3.338     2.191:  56%|#####6    | 54/96 [00:1\n",
      "       9/9     1.799    0.9595     3.338     2.191:  57%|#####7    | 55/96 [00:1\n",
      "       9/9     1.801    0.9591     3.337      2.19:  57%|#####7    | 55/96 [00:1\n",
      "       9/9     1.801    0.9591     3.337      2.19:  58%|#####8    | 56/96 [00:1\n",
      "       9/9       1.8    0.9561     3.334     2.192:  58%|#####8    | 56/96 [00:1\n",
      "       9/9       1.8    0.9561     3.334     2.192:  59%|#####9    | 57/96 [00:1\n",
      "       9/9     1.801    0.9589     3.335     2.184:  59%|#####9    | 57/96 [00:1\n",
      "       9/9     1.801    0.9589     3.335     2.184:  60%|######    | 58/96 [00:1\n",
      "       9/9     1.801    0.9589     3.335     2.184:  60%|######    | 58/96 [00:1\n",
      "       9/9     1.801    0.9589     3.335     2.184:  61%|######1   | 59/96 [00:1\n",
      "       9/9     1.798    0.9585     3.333     2.181:  61%|######1   | 59/96 [00:1\n",
      "       9/9     1.798    0.9585     3.333     2.181:  62%|######2   | 60/96 [00:1\n",
      "       9/9     1.794    0.9596     3.329     2.179:  62%|######2   | 60/96 [00:1\n",
      "       9/9     1.794    0.9596     3.329     2.179:  64%|######3   | 61/96 [00:1\n",
      "       9/9     1.797    0.9597     3.329     2.176:  64%|######3   | 61/96 [00:1\n",
      "       9/9     1.797    0.9597     3.329     2.176:  65%|######4   | 62/96 [00:1\n",
      "       9/9     1.795    0.9576     3.327     2.179:  65%|######4   | 62/96 [00:1\n",
      "       9/9     1.795    0.9576     3.327     2.179:  66%|######5   | 63/96 [00:1\n",
      "       9/9     1.799    0.9576     3.331     2.181:  66%|######5   | 63/96 [00:1\n",
      "       9/9     1.799    0.9576     3.331     2.181:  67%|######6   | 64/96 [00:1\n",
      "       9/9     1.798    0.9549     3.328     2.178:  67%|######6   | 64/96 [00:1\n",
      "       9/9     1.798    0.9549     3.328     2.178:  68%|######7   | 65/96 [00:1\n",
      "       9/9     1.801    0.9563     3.328     2.176:  68%|######7   | 65/96 [00:1\n",
      "       9/9     1.801    0.9563     3.328     2.176:  69%|######8   | 66/96 [00:1\n",
      "       9/9     1.799    0.9561     3.325     2.177:  69%|######8   | 66/96 [00:1\n",
      "       9/9     1.799    0.9561     3.325     2.177:  70%|######9   | 67/96 [00:1\n",
      "       9/9     1.798    0.9542     3.321     2.181:  70%|######9   | 67/96 [00:1\n",
      "       9/9     1.798    0.9542     3.321     2.181:  71%|#######   | 68/96 [00:1\n",
      "       9/9     1.794    0.9521     3.321     2.181:  71%|#######   | 68/96 [00:1\n",
      "       9/9     1.794    0.9521     3.321     2.181:  72%|#######1  | 69/96 [00:1\n",
      "       9/9     1.793    0.9515      3.32     2.178:  72%|#######1  | 69/96 [00:1\n",
      "       9/9     1.793    0.9515      3.32     2.178:  73%|#######2  | 70/96 [00:1\n",
      "       9/9     1.787    0.9506     3.318     2.173:  73%|#######2  | 70/96 [00:1\n",
      "       9/9     1.787    0.9506     3.318     2.173:  74%|#######3  | 71/96 [00:1\n",
      "       9/9     1.793     0.953     3.322     2.176:  74%|#######3  | 71/96 [00:1\n",
      "       9/9     1.793     0.953     3.322     2.176:  75%|#######5  | 72/96 [00:1\n",
      "       9/9     1.792    0.9529     3.321     2.177:  75%|#######5  | 72/96 [00:1\n",
      "       9/9     1.792    0.9529     3.321     2.177:  76%|#######6  | 73/96 [00:1\n",
      "       9/9     1.795    0.9521     3.318     2.174:  76%|#######6  | 73/96 [00:1\n",
      "       9/9     1.795    0.9521     3.318     2.174:  77%|#######7  | 74/96 [00:1\n",
      "       9/9     1.796    0.9506     3.316     2.174:  77%|#######7  | 74/96 [00:1\n",
      "       9/9     1.796    0.9506     3.316     2.174:  78%|#######8  | 75/96 [00:1\n",
      "       9/9     1.794    0.9525     3.321     2.176:  78%|#######8  | 75/96 [00:1\n",
      "       9/9     1.794    0.9525     3.321     2.176:  79%|#######9  | 76/96 [00:1\n",
      "       9/9     1.797    0.9538     3.322     2.178:  79%|#######9  | 76/96 [00:1\n",
      "       9/9     1.797    0.9538     3.322     2.178:  80%|########  | 77/96 [00:1\n",
      "       9/9     1.796    0.9549     3.322      2.18:  80%|########  | 77/96 [00:1\n",
      "       9/9     1.796    0.9549     3.322      2.18:  81%|########1 | 78/96 [00:1\n",
      "       9/9     1.801    0.9548     3.322     2.179:  81%|########1 | 78/96 [00:1\n",
      "       9/9     1.801    0.9548     3.322     2.179:  82%|########2 | 79/96 [00:1\n",
      "       9/9     1.802    0.9553     3.325     2.178:  82%|########2 | 79/96 [00:1\n",
      "       9/9     1.802    0.9553     3.325     2.178:  83%|########3 | 80/96 [00:1\n",
      "       9/9     1.802    0.9558     3.322     2.176:  83%|########3 | 80/96 [00:1\n",
      "       9/9     1.802    0.9558     3.322     2.176:  84%|########4 | 81/96 [00:1\n",
      "       9/9     1.802     0.957     3.322     2.176:  84%|########4 | 81/96 [00:1\n",
      "       9/9     1.802     0.957     3.322     2.176:  85%|########5 | 82/96 [00:1\n",
      "       9/9     1.801    0.9558      3.32     2.176:  85%|########5 | 82/96 [00:1\n",
      "       9/9     1.801    0.9558      3.32     2.176:  86%|########6 | 83/96 [00:1\n",
      "       9/9     1.798    0.9536     3.317     2.175:  86%|########6 | 83/96 [00:1\n",
      "       9/9     1.798    0.9536     3.317     2.175:  88%|########7 | 84/96 [00:1\n",
      "       9/9     1.794    0.9532     3.316     2.174:  88%|########7 | 84/96 [00:2\n",
      "       9/9     1.794    0.9532     3.316     2.174:  89%|########8 | 85/96 [00:2\n",
      "       9/9     1.793    0.9524     3.321     2.177:  89%|########8 | 85/96 [00:2\n",
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      "       9/9     1.792    0.9533     3.322     2.177:  91%|######### | 87/96 [00:2\n",
      "       9/9     1.789    0.9518      3.32     2.176:  91%|######### | 87/96 [00:2\n",
      "       9/9     1.789    0.9518      3.32     2.176:  92%|#########1| 88/96 [00:2\n",
      "       9/9     1.788    0.9512     3.321     2.177:  92%|#########1| 88/96 [00:2\n",
      "       9/9     1.788    0.9512     3.321     2.177:  93%|#########2| 89/96 [00:2\n",
      "       9/9     1.786      0.95     3.321     2.181:  93%|#########2| 89/96 [00:2\n",
      "       9/9     1.786      0.95     3.321     2.181:  94%|#########3| 90/96 [00:2\n",
      "       9/9     1.784    0.9494     3.322     2.179:  94%|#########3| 90/96 [00:2\n",
      "       9/9     1.784    0.9494     3.322     2.179:  95%|#########4| 91/96 [00:2\n",
      "       9/9      1.78    0.9476      3.32     2.178:  95%|#########4| 91/96 [00:2\n",
      "       9/9      1.78    0.9476      3.32     2.178:  96%|#########5| 92/96 [00:2\n",
      "       9/9     1.779    0.9467     3.319      2.18:  96%|#########5| 92/96 [00:2\n",
      "       9/9     1.779    0.9467     3.319      2.18:  97%|#########6| 93/96 [00:2\n",
      "       9/9     1.778    0.9469      3.32     2.179:  97%|#########6| 93/96 [00:2\n",
      "       9/9     1.778    0.9469      3.32     2.179:  98%|#########7| 94/96 [00:2\n",
      "       9/9     1.779    0.9479     3.323     2.178:  98%|#########7| 94/96 [00:2\n",
      "       9/9     1.779    0.9479     3.323     2.178:  99%|#########8| 95/96 [00:2\n",
      "       9/9     1.778    0.9477     3.323     2.181:  99%|#########8| 95/96 [00:2\n",
      "       9/9     1.778    0.9477     3.323     2.181: 100%|##########| 96/96 [00:2\n",
      "       9/9     1.778    0.9477     3.323     2.181: 100%|##########| 96/96 [00:2\n",
      "\n",
      "Inferencing model in val datasets.:   0%|                 | 0/8 [00:00<?, ?it/s]\n",
      "Inferencing model in val datasets.:  12%|#1       | 1/8 [00:00<00:06,  1.01it/s]\n",
      "Inferencing model in val datasets.:  25%|##2      | 2/8 [00:01<00:05,  1.15it/s]\n",
      "Inferencing model in val datasets.:  38%|###3     | 3/8 [00:02<00:04,  1.03it/s]\n",
      "Inferencing model in val datasets.:  50%|####5    | 4/8 [00:03<00:03,  1.07it/s]\n",
      "Inferencing model in val datasets.:  62%|#####6   | 5/8 [00:04<00:02,  1.10it/s]\n",
      "Inferencing model in val datasets.:  75%|######7  | 6/8 [00:05<00:01,  1.13it/s]\n",
      "Inferencing model in val datasets.:  88%|#######8 | 7/8 [00:06<00:00,  1.05it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.27it/s]\n",
      "Inferencing model in val datasets.: 100%|#########| 8/8 [00:06<00:00,  1.15it/s]\n",
      "\n",
      "Evaluating speed.\n",
      "\n",
      "Evaluating mAP by pycocotools.\n",
      "Saving runs\\train\\exp1\\predictions.json...\n",
      "Epoch: 9 | mAP@0.5: 0.28033424759289327 | mAP@0.50:0.95: 0.21239362622131772\n",
      "\n",
      "Training completed in 0.091 hours.\n"
     ]
    }
   ],
   "source": [
    "#!python tools/train.py --batch 16 --conf configs/yolov6s_finetune.py --data dataset.yaml --device 0 --epochs 50 --eval-interval 2\n",
    "\n",
    "!python tools/train.py --batch 16 --conf configs/yolov6s_finetune.py --data-path dataset.yaml --device 0 --epochs 10 --eval-interval 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7fddc7",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4edbd0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!python tools/eval.py --data dataset.yaml  --weights run/train/exp7/weights/best_ckpt.pt --device 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c42f74",
   "metadata": {},
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a4b61ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Namespace(agnostic_nms=False, classes=None, conf_thres=0.25, device='0', half=False, hide_conf=False, hide_labels=False, img_size=640, iou_thres=0.45, max_det=1000, name='exp', project='runs/inference', save_img=True, save_txt=False, source='test_images/1.jpg', weights='runs/train/exp7/weights/best_ckpt.pt', yaml='dataset.yaml')\n",
      "Save directory already existed\n",
      "Loading checkpoint from runs/train/exp7/weights/best_ckpt.pt\n",
      "\n",
      "Fusing model...\n",
      "\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "100%|##########| 1/1 [00:00<00:00, 32.26it/s]\n",
      "Results saved to runs/inference\\exp\n"
     ]
    }
   ],
   "source": [
    "#cd YOLOv6\n",
    "!python tools/infer.py --weights runs/train/exp7/weights/best_ckpt.pt --source test_images/1.jpg --yaml dataset.yaml --device 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "920c8811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "image/jpeg": {
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image #this is to render predictions\n",
    "Image(filename='runs/inference/exp/1.jpg', width=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf1075e4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}