timm
PyTorch Image Models — largest collection of pretrained computer vision models for PyTorch. timm features: 1000+ pretrained models (ViT, EfficientNet, ConvNeXt, Swin Transformer, DeiT, EVA, MetaFormer), timm.create_model() factory with pretrained=True, feature extraction (features_only=True), custom classifier heads, timm.data.create_transform for optimal preprocessing per model, model listings (timm.list_models()), HuggingFace Hub integration (timm.create_model('hf-hub:timm/model')), and benchmark data for model selection. Maintained by Ross Wightman at Hugging Face. Premier library for vision transfer learning in PyTorch.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Model weights from HuggingFace Hub with HTTPS. Weights are safetensors or pickle-based — prefer safetensors format for security. Validate model source before loading in security-sensitive agent pipelines.
⚡ Reliability
Best When
Fine-tuning or feature extraction for PyTorch agent vision tasks where you need access to the latest SOTA models (ViT, ConvNeXt, Swin, EVA) with pretrained weights and optimal preprocessing configurations — timm has larger model zoo than torchvision with more cutting-edge architectures.
Avoid When
You're not using PyTorch, need detection/segmentation (combine with detectron2), or only need the standard ResNet/EfficientNet models (torchvision covers those).
Use Cases
- • Agent vision feature extractor — model = timm.create_model('convnext_base', pretrained=True, features_only=True); features = model(images) — extract multi-scale visual features for agent vision pipeline; ConvNeXt backbone provides powerful features without classification head
- • Agent model selection — models = timm.list_models('efficientnet*', pretrained=True); print(timm.model_info('efficientnet_b4')) — discover available pretrained models; agent selects model based on accuracy/speed tradeoff from timm benchmark data; 1000+ models with accuracy benchmarks
- • Agent fine-tuning — model = timm.create_model('vit_base_patch16_224', pretrained=True, num_classes=len(agent_classes)); transform = timm.data.create_transform(**timm.data.resolve_model_data_config(model)) — ViT fine-tuned for agent-specific classification; create_transform uses model's optimal preprocessing config
- • Agent image embedding — model = timm.create_model('eva02_large_patch14_448', pretrained=True, num_classes=0); embeddings = model(images) — num_classes=0 returns global average pooled features; agent visual search uses cosine similarity of timm embeddings; EVA models provide SOTA visual features
- • Agent custom backbone — backbone = timm.create_model('swin_base_patch4_window7_224', pretrained=True, features_only=True, out_indices=(2, 3)); agent object detector uses Swin Transformer backbone with multi-scale features; out_indices selects which stages to return
Not For
- • Non-PyTorch frameworks — timm is PyTorch-only; for TensorFlow/Keras vision use keras.applications; for JAX use Flax/Scenic
- • Object detection/segmentation models — timm is primarily classification backbones; for detection/segmentation use torchvision or detectron2 with timm backbone
- • Video understanding — timm focuses on 2D image models; for video use TimeSformer or VideoMAE
Interface
Authentication
No auth for public models. Private HuggingFace Hub models require HF_TOKEN. Model weights download automatically on first use.
Pricing
timm is Apache 2.0 licensed. Model weights on HuggingFace Hub are individually licensed (most Apache/MIT). No API costs.
Agent Metadata
Known Gotchas
- ⚠ Each model has specific input resolution — timm.create_model('vit_base_patch16_224') expects 224x224 input; feeding different resolution produces wrong results or shape errors; always use timm.data.create_transform(**timm.data.resolve_model_data_config(model)) to get model-specific preprocessing including correct resolution
- ⚠ features_only=True changes output shape — timm.create_model('resnet50', features_only=True) returns list of feature maps, not single tensor; agent code expecting tensor output gets TypeError from list; check model.feature_info for output shape of each stage when using features_only
- ⚠ model weights may not match timm version — timm.create_model('model_name', pretrained=True) downloads weights from HuggingFace Hub; timm version upgrade may add new model variants with same base name but different weights; pin timm version in agent training to prevent weight drift between runs
- ⚠ num_classes=0 vs features_only differ — num_classes=0 removes classification head and returns global pooled features (single vector per image); features_only=True returns multi-scale feature maps (multiple tensors); agent embedding tasks use num_classes=0; agent detection backbones use features_only=True; they are different APIs
- ⚠ Mixed precision requires explicit setup — timm models work with torch.cuda.amp.autocast() but require model = model.to(torch.float16) or autocast context; some timm models have LayerNorm that doesn't work in FP16 without fused implementation; test agent mixed precision training on specific timm model before large training run
- ⚠ Model listing may include models without pretrained weights — timm.list_models('vit*', pretrained=True) filters to pretrained-only; timm.list_models('vit*') includes models without pretrained weights; agent code using untrained models for transfer learning gets random initialization; always pass pretrained=True filter
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for timm.
Scores are editorial opinions as of 2026-03-06.