# 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. - [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