huggingface/pytorch-image-models

What is PyTorch Image Models (timm)?

timm is a library of ready‑to‑use computer‑vision models built on top of PyTorch. It bundles:

• Hundreds of state‑of‑the‑art image architectures – from classic ConvNets (ResNet, EfficientNet, MobileNet) to modern Vision Transformers (ViT, EVA, BEiT, ConvNeXt, etc.).
• Pre‑trained weights for most of those models, usually trained on ImageNet‑1k, so you can start using them for inference or fine‑tuning right away.
• Utility code – layers, optimizers, learning‑rate schedulers, data loaders, augmentation pipelines, and reference training/validation scripts that let you reproduce published results with minimal setup.
• Training helpers – a new sweep.py for hyper‑parameter sweeps, a flexible train.py that supports resuming, multi‑GPU runs, multi‑label classification, custom loss functions, and more.

In short, timm is a one‑stop shop for researchers and engineers who need a wide variety of image models without having to re‑implement each architecture from scratch.

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Who might find it useful?

Audience	Why it matters
ML researchers	Quickly prototype new ideas on top of a large catalog of SOTA vision models and compare results.
Practitioners / engineers	Pull a pre‑trained model into a production pipeline with a single import, then fine‑tune on a custom dataset.
Students / hobbyists	Learn about modern vision architectures by reading clean, well‑documented PyTorch code.
Benchmarking teams	Use the provided training scripts and benchmark CSVs to measure speed/accuracy on different GPUs.

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Core features (as described in the README)

Feature	Details
Model zoo	> 300 architectures, each with variant‑specific pretrained weights. Includes ConvNets, Transformers, hybrid models, and niche backbones like CPUBone for low‑power devices.
Training scripts	train.py and validate.py support resume, epoch stopping, milestone checkpoints, YAML config validation, multi‑label tasks, custom loss functions, and mixed‑precision.
Hyper‑parameter sweeps	New sweep.py runs grid or random searches over any train.py argument, distributes jobs across local GPUs, and can resume interrupted sweeps.
Optimizers & schedulers	Updated implementations for RMSpropTF, LaProp, LAMB, LARS, AdaBelief, NovoGrad, Muon, etc., plus a warmup‑stable‑decay (WSD) LR scheduler.
Data utilities	Flexible image datasets, multi‑label support, Mixup/CutMix, AugMix, and automatic handling of non‑square or single‑channel images.
Model resizing	set_input_size() lets most models adapt to different image resolutions on the fly, with special handling for windowed Vision Transformers.
Export & inference	ONNX export compatibility, inference.py with automatic input‑size selection, and support for Torch‑compile and CUDA graphs.
Documentation	Model API pages, training guides, and a curated list of awesome PyTorch resources.
Licensing & citation	Clear license information for each model and instructions on how to cite the library.

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How to get started (quick‑start steps)

1. Install

   pip install timm
   

2. Load a model (e.g., a pretrained EfficientNet‑B0)

   import timm
   model = timm.create_model('efficientnet_b0', pretrained=True)
   model.eval()
   

3. Run inference

   from torchvision import transforms
   from PIL import Image
   
   preprocess = transforms.Compose([
       transforms.Resize(256),
       transforms.CenterCrop(224),
       transforms.ToTensor(),
       transforms.Normalize(mean=[0.485, 0.456, 0.406],
                            std=[0.229, 0.224, 0.225]),
   ])
   
   img = Image.open('my_image.jpg')
   input_tensor = preprocess(img).unsqueeze(0)  # batch dim
   with torch.no_grad():
       logits = model(input_tensor)
   

4. Fine‑tune – use the provided train.py script, point it at your dataset, and optionally run a sweep:

   python train.py --model efficientnet_b0 --data-dir /path/to/data --epochs 30
   python sweep.py --config my_sweep.yaml
   

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When to consider alternatives

• If you need non‑vision models (LLMs, audio, RL agents), timm does not cover those domains.
• For very large‑scale training (billions of images) you may want a dedicated distributed training framework (e.g., DeepSpeed, Lightning) that integrates with timm but provides extra scaling features.
• If you require on‑device inference (mobile/embedded), look at the lightweight backbones like CPUBone or MobileNet‑V2, but you may still need to convert the model to ONNX/TFLite separately.

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TL;DR – timm is a comprehensive, actively maintained collection of image models and training utilities for PyTorch, ideal for anyone wanting to experiment with or deploy state‑of‑the‑art computer‑vision architectures.