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# TimmWrapper


## Overview

Helper class to enable loading timm models to be used with the transformers library and its autoclasses.

```python
from urllib.request import urlopen

import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForImageClassification


# 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( device_map="auto")

# 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.

<PipelineTag pipeline="image-classification"/>

- [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
