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dnth committed Oct 21, 2024
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</div>


## Why x.infer?
## 🤔 Why x.infer?
If you'd like to run many models from different libraries without having to rewrite your inference code, x.infer is for you. It has a simple API and is easy to extend. Currently supports Transformers, Ultralytics, and TIMM.

Have a custom model? Create a class that implements the `BaseModel` interface and register it with x.infer. See [Adding New Models](#adding-new-models) for more details.

## Key Features
## 🌟 Key Features
<div align="center">
<img src="https://raw.githubusercontent.com/dnth/x.infer/refs/heads/main/assets/flowchart.gif" alt="x.infer" width="500"/>
</div>
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- **Ease of Use:** Simplifies model loading, input preprocessing, inference execution, and output postprocessing.
- **Extensibility:** Add support for new models and libraries with minimal code changes.

## Quickstart
## 🚀 Quickstart

Here's a quick example demonstrating how to use x.infer with a Transformers model:

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└────────────────┴─────────────────────────────────────────────────┴─────────────────────┘
```

## Launch Gradio Interface
## 🖥️ Launch Gradio Interface

```python
model.launch_gradio()
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![Gradio Interface](https://raw.githubusercontent.com/dnth/x.infer/refs/heads/main/assets/gradio.png)


## Installation
## 📦 Installation
> [!IMPORTANT]
> You must have [PyTorch](https://pytorch.org/get-started/locally/) installed to use x.infer.
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pip install -e .
```

## Usage
## 🛠️ Usage


### Supported Models
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```


### Adding New Models
### 🔧 Adding New Models

+ **Step 1:** Create a new model class that implements the `BaseModel` interface.

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