Code and documentation to train Stanford's Alpaca models, and generate the data.
Open source
24 alternativestimm
timm provides PyTorch image encoders and backbones along with training, evaluation, inference, and export scripts.
About timm
timm is an open-source repository of PyTorch image encoders and backbones with pretrained weights, covering architectures such as ResNet, EfficientNet, Vision Transformer (ViT), Swin Transformer, and ConvNeXt. It provides scripts for training, validation, inference, and export, including support for multi-label classification tasks and additional loss options.
Key features
- PyTorch image encoders and backbones with pretrained weights
- Training script train.py with multi-label classification task support
- Validation script validate.py reporting mAP and F1 metrics
- Inference script inference.py for running models
- ONNX export and validation via onnx_export.py and onnx_validate.py
- Benchmarking and checkpoint utilities including benchmark.py and avg_checkpoints.py
Best for: Python developers and ML researchers training or evaluating PyTorch image models.
Good for
- Open source with training, validation, inference, and export scripts
- Provides pretrained weights across many image encoder architectures
- Supports multi-label classification with mAP and F1 reporting
timm at a glance
- Category
- AI & Machine Learning › Models & ML Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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timm FAQ
What model architectures are included?
ResNet, ResNeXT, EfficientNet, Vision Transformer (ViT), Swin Transformer, ConvNeXt, and others, with pretrained weights.
Does timm support multi-label classification?
Yes. train.py and validate.py support --task multilabel, trained with BCE, with mAP and F1 reporting during validation.
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