huggingface/pytorch-image-models

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37099 stars · 5192 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PyTorch Image Models (timm) is a Python library offering the largest collection of PyTorch image encoder/backbone architectures with 700+ pretrained weights, plus layers, optimizers, schedulers, augmentations, and training/eval scripts. It provides a unified API (create_model) for loading, fine-tuning, and extracting features from models like ResNet, EfficientNet, ViT, Swin, and ConvNeXt.

Use cases

• load a pretrained image classification model in pytorch
• extract image features with a vision backbone
• fine-tune a vision transformer on my own dataset
• train an image classifier on imagenet with distributed training
• find the best pretrained cnn or vit weights for transfer learning
• export a pytorch image model for inference
• compare efficientnet resnet convnext model performance

When to choose

• you need a wide variety of image model architectures with pretrained weights in PyTorch
• you want a consistent API for training, inference, and feature extraction across vision backbones
• you need state-of-the-art vision models like ViT, Swin, ConvNeXt, or EfficientNet without reimplementing them

When to avoid

• you work outside PyTorch (e.g., TensorFlow or JAX)
• you need non-image tasks like NLP or audio
• you want a no-code or hosted inference service rather than a Python library

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision sdk deep-learning computer-vision image-processing machine-learning python cross-platform pytorch timm pretrained-models vision-transformers image-classification model-zoo transfer-learning feature-extraction gpu

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