# TimmWrapper
## Overview
Helper class to enable loading timm models to be used with the transformers library and its autoclasses.
```python
>>> import torch
>>> from PIL import Image
>>> from urllib.request import urlopen
>>> from transformers import AutoModelForImageClassification, AutoImageProcessor
>>> # Load image
>>> image = Image.open(urlopen(
... 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
... ))
>>> # Load model and image processor
>>> checkpoint = "timm/resnet50.a1_in1k"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
>>> model = AutoModelForImageClassification.from_pretrained(checkpoint).eval()
>>> # Preprocess image
>>> inputs = image_processor(image)
>>> # Forward pass
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # Get top 5 predictions
>>> top5_probabilities, top5_class_indices = torch.topk(logits.softmax(dim=1) * 100, k=5)
```
## Resources:
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with TimmWrapper.
- [Collection of Example Notebook](https://github.com/ariG23498/timm-wrapper-examples) 🌎
> [!TIP]
> For a more detailed overview please read the [official blog post](https://huggingface.co/blog/timm-transformers) on the timm integration.
## TimmWrapperConfig
[[autodoc]] TimmWrapperConfig
## TimmWrapperImageProcessor
[[autodoc]] TimmWrapperImageProcessor
- preprocess
## TimmWrapperModel
[[autodoc]] TimmWrapperModel
- forward
## TimmWrapperForImageClassification
[[autodoc]] TimmWrapperForImageClassification
- forward
