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What Is New

  • 2023.07.01: 🔥新增语言模型 (包括热点模型glm/llama/bloom 来自于mindformers套件)
  • 2023.06.01: 我们对经典SOTA模型进行了重构，模块化数据处理，模型定义，训练流程等常用组件，推出MindSpore CV/NLP/Audio/Yolo/OCR等系列
  • 原models仓模型实现是基于MindSpore原生API，并且有一定训练推理加速优化
  • 更多关于模型精度性能信息，请查阅 benchmark。

官方标准模型

计算机视觉

图像分类（骨干类)

model acc@1 mindcv recipe vanilla mindspore
vgg11 71.86 config
vgg13 72.87 config
vgg16 74.61 config link
vgg19 75.21 config link
resnet18 70.21 config link
resnet34 74.15 config link
resnet50 76.69 config link
resnet101 78.24 config link
resnet152 78.72 config link
resnetv2_50 76.90 config
resnetv2_101 78.48 config
dpn92 79.46 config
dpn98 79.94 config
dpn107 80.05 config
dpn131 80.07 config
densenet121 75.64 config
densenet161 79.09 config
densenet169 77.26 config
densenet201 78.14 config
seresnet18 71.81 config
seresnet34 75.36 config
seresnet50 78.31 config
seresnext26 77.18 config
seresnext50 78.71 config
skresnet18 73.09 config
skresnet34 76.71 config
skresnet50_32x4d 79.08 config
resnext50_32x4d 78.53 config
resnext101_32x4d 79.83 config
resnext101_64x4d 80.30 config
resnext152_64x4d 80.52 config
rexnet_x09 77.07 config
rexnet_x10 77.38 config
rexnet_x13 79.06 config
rexnet_x15 79.94 config
rexnet_x20 80.64 config
resnest50 80.81 config
resnest101 82.50 config
res2net50 79.35 config
res2net101 79.56 config
res2net50_v1b 80.32 config
res2net101_v1b 95.41 config
googlenet 72.68 config
inceptionv3 79.11 config link
inceptionv4 80.88 config link
mobilenet_v1_025 53.87 config
mobilenet_v1_050 65.94 config
mobilenet_v1_075 70.44 config
mobilenet_v1_100 72.95 config
mobilenet_v2_075 69.98 config
mobilenet_v2_100 72.27 config
mobilenet_v2_140 75.56 config
mobilenet_v3_small 68.10 config
mobilenet_v3_large 75.23 config link
shufflenet_v1_g3_x0_5 57.05 config
shufflenet_v1_g3_x1_5 67.77 config link
shufflenet_v2_x0_5 57.05 config
shufflenet_v2_x1_0 67.77 config link
shufflenet_v2_x1_5 57.05 config
shufflenet_v2_x2_0 67.77 config
xception 79.01 config link
ghostnet_50 66.03 config
ghostnet_100 73.78 config
ghostnet_130 75.50 config
nasnet_a_4x1056 73.65 config
mnasnet_0.5 68.07 config
mnasnet_0.75 71.81 config
mnasnet_1.0 74.28 config
mnasnet_1.4 76.01 config
efficientnet_b0 76.89 config link
efficientnet_b1 78.95 config link
efficientnet_b2 79.80 link
efficientnet_b3 80.50 link
efficientnet_v2 83.77 link
regnet_x_200mf 68.74 config
regnet_x_400mf 73.16 config
regnet_x_600mf 73.34 config
regnet_x_800mf 76.04 config
regnet_y_200mf 70.30 config
regnet_y_400mf 73.91 config
regnet_y_600mf 75.69 config
regnet_y_800mf 76.52 config
mixnet_s 75.52 config
mixnet_m 76.64 config
mixnet_l 78.73 config
hrnet_w32 80.64 config
hrnet_w48 81.19 config
bit_resnet50 76.81 config
bit_resnet50x3 80.63 config
bit_resnet101 77.93 config
repvgg_a0 72.19 config
repvgg_a1 74.19 config
repvgg_a2 76.63 config
repvgg_b0 74.99 config
repvgg_b1 78.81 config
repvgg_b2 79.29 config
repvgg_b3 80.46 config
repvgg_b1g2 78.03 config
repvgg_b1g4 77.64 config
repvgg_b2g4 78.80 config
repmlp_t224 76.71 config
convnext_tiny 81.91 config
convnext_small 83.40 config
convnext_base 83.32 config
vit_b_32_224 75.86 config link
vit_l_16_224 76.34 config
vit_l_32_224 73.71 config
swintransformer_tiny 80.82 config link
pvt_tiny 74.81 config
pvt_small 79.66 config
pvt_medium 81.82 config
pvt_large 81.75 config
pvt_v2_b0 71.50 config
pvt_v2_b1 78.91 config
pvt_v2_b2 81.99 config
pvt_v2_b3 82.84 config
pvt_v2_b4 83.14 config
pit_ti 72.96 config
pit_xs 78.41 config
pit_s 80.56 config
pit_b 81.87 config
coat_lite_tiny 77.35 config
coat_lite_mini 78.51 config
coat_tiny 79.67 config
convit_tiny 73.66 config
convit_tiny_plus 77.00 config
convit_small 81.63 config
convit_small_plus 81.80 config
convit_base 82.10 config
convit_base_plus 81.96 config
crossvit_9 73.56 config
crossvit_15 81.08 config
crossvit_18 81.93 config
mobilevit_xx_small 68.90 config
mobilevit_x_small 74.98 config
mobilevit_small 78.48 config
visformer_tiny 78.28 config
visformer_tiny_v2 78.82 config
visformer_small 81.76 config
visformer_small_v2 82.17 config
edgenext_xx_small 71.02 config
edgenext_x_small 75.14 config
edgenext_small 79.15 config
edgenext_base 82.24 config
poolformer_s12 77.33 config
xcit_tiny_12_p16 77.67 config

目标检测

yolo

model map mindyolo recipe vanilla mindspore
yolov8_n 37.2 config
yolov8_s 44.6 config
yolov8_m 50.5 config
yolov8_l 52.8 config
yolov8_x 53.7 config
yolov7_t 37.5 config
yolov7_l 50.8 config
yolov7_x 52.4 config
yolov5_n 27.3 config
yolov5_s 37.6 config link
yolov5_m 44.9 config
yolov5_l 48.5 config
yolov5_x 50.5 config
yolov4_csp 45.4 config
yolov4_csp(silu) 45.8 config link
yolov3_darknet53 45.5 config link
yolox_n 24.1 config
yolox_t 33.3 config
yolox_s 40.7 config
yolox_m 46.7 config
yolox_l 49.2 config
yolox_x 51.6 config
yolox_darknet53 47.7 config

经典

model map mind_series recipe vanilla mindspore
ssd_vgg16 23.2 link
ssd_mobilenetv1 22.0 link
ssd_mobilenetv2 29.1 link
ssd_resnet50 34.3 link
fastrcnn 58 link
maskrcnn_mobilenetv1 coming soon link
maskrcnn_resnet50 coming soon link

语义分割

model mind_series recipe vanilla mindspore
ocrnet link
deeplab v3 link
deeplab v3 plus link
unet link
unet3d link

OCR

文本检测

model dataset fscore mindocr recipe vanilla mindspore
dbnet_mobilenetv3 icdar2015 77.23 config link
dbnet_resnet18 icdar2015 81.73 config link
dbnet_resnet50 icdar2015 85.05 config link
dbnet++_resnet50 icdar2015 86.74 config
psenet_resnet152 icdar2015 82.06 config link
east_resnet50 icdar2015 84.87 config link
fcenet_resnet50 icdar2015 84.12 config

文本识别

model dataset acc mindocr recipe vanilla mindspore
svtr_tiny IC03,13,15,IIIT,etc 89.02 config
crnn_vgg7 IC03,13,15,IIIT,etc 82.03 config link
crnn_resnet34_vd IC03,13,15,IIIT,etc 84.45 config
rare_resnet34_vd IC03,13,15,IIIT,etc 85.19 config link

文本方向分类

model dataset acc mindocr recipe
mobilenetv3 RCTW17,MTWI,LSVT 94.59 config

人脸

model dataset acc mindface recipe vanilla mindspore
arcface_mobilefacenet-0.45g MS1MV2 98.70 config
arcface_r50 MS1MV2 99.76 config
arcface_r100 MS1MV2 99.38 config link
arcface_vit_t MS1MV2 99.71 config
arcface_vit_s MS1MV2 99.76 config
arcface_vit_b MS1MV2 99.81 config
arcface_vit_l MS1MV2 99.75 config
retinaface_mobilenet_0.25 WiderFace 90.77/88.2/74.76 config link
retinaface_r50 WiderFace 95.07/93.61/84.84 config link

语言模型

model mindformer recipe vanilla mindspore
bert_base config link
t5_small config
gpt2_small config
gpt2_13b config
gpt2_52b config
pangu_alpha config
glm_6b config
glm_6b_lora config
llama_7b config
llama_13b config
llama_65b config
llama_7b_lora config
bloom_560m config
bloom_7.1b config
bloom_65b config
bloom_176b config

免责声明

MindSpore仅提供下载和预处理公共数据集的脚本。我们不拥有这些数据集，也不对它们的质量负责或维护。请确保您具有在数据集许可下使用该数据集的权限。在这些数据集上训练的模型仅用于非商业研究和教学目的。

致数据集拥有者：如果您不希望将数据集包含在MindSpore中，或者希望以任何方式对其进行更新，我们将根据要求删除或更新所有公共内容。请通过GitHub或Gitee与我们联系。非常感谢您对这个社区的理解和贡献。

许可证

Apache 2.0许可证