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Handles checkpoint loading, device placement, and inference-mode setup in a single call, abstracting away boilerplate PyTorch initialization.","intents":["I want to quickly load a pre-trained ResNet50 or ViT model without manually downloading weights","I need to benchmark multiple vision architectures on my dataset without writing initialization code for each","I want to use state-of-the-art models like DeiT or CLIP variants without hunting for official implementations"],"best_for":["computer vision researchers prototyping model comparisons","ML engineers building production image classification pipelines","teams migrating from TensorFlow to PyTorch and needing model parity"],"limitations":["Model registry is PyTorch-only; no TensorFlow or ONNX export built-in","Automatic weight caching requires ~50GB+ disk space for full model zoo","No built-in quantization or pruning — requires external tools for model compression","Inference speed varies significantly by architecture; no automatic hardware optimization (e.g., TensorRT)"],"requires":["Python 3.7+","PyTorch 1.9+","Internet connection for initial weight download","~2-10GB GPU VRAM depending on model size"],"input_types":["model name string (e.g., 'resnet50', 'vit_base_patch16_224')","PyTorch tensor (B, 3, H, W)"],"output_types":["PyTorch nn.Module instance","logits tensor (B, num_classes)"],"categories":["image-visual","model-zoo"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_1","uri":"capability://image.visual.image.preprocessing.and.augmentation.pipeline","name":"image preprocessing and augmentation pipeline","description":"Provides composable image transforms (resize, normalization, augmentation) optimized for vision models with automatic resolution inference from model metadata. Uses PyTorch's torchvision.transforms as a base but adds model-specific defaults (e.g., ImageNet normalization stats, optimal input sizes) and integrates with timm's model registry to auto-configure preprocessing for any loaded model. Supports both training (with augmentation) and inference modes.","intents":["I want to apply the correct preprocessing (normalization, resize) for a specific model without looking up its training config","I need to build a training pipeline with augmentation that matches the model's original training setup","I want to apply consistent preprocessing across multiple models for fair comparison"],"best_for":["ML practitioners building end-to-end training scripts","researchers comparing models with controlled preprocessing","teams standardizing data pipelines across projects"],"limitations":["Augmentation is CPU-bound; GPU-accelerated augmentation (e.g., Kornia) requires manual integration","Limited to 2D image transforms; no 3D medical imaging or video preprocessing","Model-specific preprocessing metadata must be manually maintained when new architectures are added"],"requires":["Python 3.7+","PyTorch 1.9+","torchvision 0.10+","Pillow for image I/O"],"input_types":["PIL Image","numpy array (H, W, 3)","PyTorch tensor"],"output_types":["PyTorch tensor (3, H, W) normalized and resized"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_10","uri":"capability://code.generation.editing.custom.model.architecture.registration.and.composition","name":"custom model architecture registration and composition","description":"Provides a plugin system for registering custom model architectures into the timm registry, enabling them to be loaded via the standard `timm.create_model()` API alongside built-in models. Uses a decorator-based registration pattern that integrates custom models with timm's preprocessing, export, and benchmarking utilities. Supports model composition (combining modules from different architectures) and automatic documentation generation.","intents":["I want to register my custom model architecture so it can be loaded with timm.create_model()","I need to combine components from different timm models (e.g., ResNet backbone + custom head)","I want my custom model to work with timm's preprocessing, export, and benchmarking tools"],"best_for":["researchers developing novel architectures and wanting to integrate with timm ecosystem","teams building domain-specific models that extend timm architectures","practitioners creating reusable model components"],"limitations":["Custom models must follow timm's interface conventions; non-standard architectures may require manual adaptation","Registration is local to the Python process; no persistent model registry across projects","No built-in versioning or dependency management for custom models","Documentation generation is basic; requires manual docstring maintenance"],"requires":["Python 3.7+","PyTorch 1.9+","timm library"],"input_types":["custom model class (nn.Module subclass)","model name (string)","metadata (dict): num_classes, input_size, etc."],"output_types":["registered model (accessible via timm.create_model())","model metadata (dict)"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_2","uri":"capability://search.retrieval.model.architecture.search.and.discovery","name":"model architecture search and discovery","description":"Provides a searchable registry of 900+ vision model architectures with filtering by family (ResNet, ViT, EfficientNet), input resolution, parameter count, and training dataset. 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Integrates with PyTorch's optimizer API to enable differential learning rates (e.g., lower LR for early layers, higher for head). Supports both full fine-tuning and adapter-style approaches via selective parameter freezing. Includes utilities for loading partial checkpoints (e.g., pre-trained backbone only) and handling shape mismatches when adapting to new classification heads.","intents":["I want to fine-tune a pre-trained model on my custom dataset with frozen backbone and trainable head","I need to apply different learning rates to different layers (discriminative fine-tuning) without manual optimizer setup","I want to load a pre-trained ImageNet model but adapt it to a different number of output classes"],"best_for":["practitioners with limited labeled data who need transfer learning","teams fine-tuning models on domain-specific datasets","researchers experimenting with layer-wise learning rate schedules"],"limitations":["No built-in support for adapter modules or LoRA-style parameter-efficient fine-tuning","Learning rate scheduling is manual; no automatic warmup or decay strategies","Checkpoint loading doesn't handle architecture mismatches (e.g., different number of heads) automatically"],"requires":["Python 3.7+","PyTorch 1.9+","timm library"],"input_types":["pre-trained model (nn.Module)","new dataset (DataLoader)","fine-tuning config (dict or argparse Namespace)"],"output_types":["fine-tuned model checkpoint","training logs (loss, accuracy)"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_4","uri":"capability://code.generation.editing.model.export.and.conversion.to.inference.formats","name":"model export and conversion to inference formats","description":"Exports PyTorch models to ONNX, TorchScript, and other inference formats with automatic shape inference and optimization. Handles model-specific export quirks (e.g., handling attention masks in Vision Transformers) and validates exported models against the original PyTorch version. Includes utilities for quantization-aware training (QAT) and post-training quantization (PTQ) to reduce model size for edge deployment.","intents":["I want to export a timm model to ONNX for inference on non-PyTorch runtimes (e.g., ONNX Runtime, TensorRT)","I need to quantize a model to int8 for mobile deployment while maintaining accuracy","I want to convert a model to TorchScript for C++ inference without Python dependencies"],"best_for":["ML engineers deploying models to production (cloud, edge, mobile)","teams optimizing models for inference latency and memory","practitioners building cross-platform inference pipelines"],"limitations":["ONNX export doesn't support all timm architectures; some custom ops require manual handling","Quantization support is basic; no built-in mixed-precision or per-channel quantization","TorchScript export may fail for models with dynamic control flow; requires manual tracing","No built-in support for model pruning or distillation"],"requires":["Python 3.7+","PyTorch 1.9+","onnx and onnxruntime for ONNX export","Optional: TensorRT for GPU optimization"],"input_types":["timm model (nn.Module)","sample input tensor (B, 3, H, W)","export format string ('onnx', 'torchscript', 'tflite')"],"output_types":["ONNX model file (.onnx)","TorchScript module (.pt)","quantized model checkpoint"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_5","uri":"capability://image.visual.batch.inference.with.automatic.batching.and.device.management","name":"batch inference with automatic batching and device management","description":"Provides utilities for efficient batch inference across multiple images with automatic GPU/CPU device placement, mixed precision (fp16/bf16) support, and memory-efficient inference modes. Handles variable-sized inputs by padding or resizing to a common shape. Includes profiling utilities to measure throughput and latency per batch size, enabling automatic batch size selection for hardware constraints.","intents":["I want to run inference on 1000s of images efficiently without manual GPU memory management","I need to find the optimal batch size for my hardware (GPU VRAM, CPU memory)","I want to use mixed precision (fp16) inference to speed up inference on modern GPUs"],"best_for":["ML engineers building inference servers or batch processing pipelines","teams optimizing inference throughput for production","practitioners with limited GPU memory who need efficient batching"],"limitations":["Automatic batch size selection requires profiling on target hardware; not portable across devices","Mixed precision inference may reduce accuracy for some models; requires validation","No built-in support for distributed inference across multiple GPUs or TPUs","Variable-sized input handling (padding/resizing) may introduce artifacts for some tasks"],"requires":["Python 3.7+","PyTorch 1.9+","CUDA 11.0+ for GPU inference","Optional: Apex for mixed precision on older GPUs"],"input_types":["list of images (PIL, numpy, or tensor)","batch size (int)","device string ('cuda', 'cpu')"],"output_types":["batch predictions (tensor B, num_classes)","profiling metrics (throughput, latency)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_6","uri":"capability://image.visual.model.ensemble.and.voting.strategies","name":"model ensemble and voting strategies","description":"Provides utilities for combining predictions from multiple models (different architectures, checkpoints, or augmentations) using voting, averaging, or learned weighting strategies. Supports test-time augmentation (TTA) by averaging predictions across multiple augmented versions of the same input. Handles ensemble-specific optimizations like shared preprocessing and batch-level parallelization across ensemble members.","intents":["I want to combine predictions from multiple models to improve accuracy without training a new model","I need to apply test-time augmentation (TTA) by averaging predictions across augmented versions","I want to weight ensemble members by their validation accuracy automatically"],"best_for":["practitioners in competitions (Kaggle) seeking accuracy gains","teams deploying models where accuracy is critical and latency is secondary","researchers studying ensemble effects on vision models"],"limitations":["Ensemble inference is N times slower than single model (no speedup)","Learned weighting requires labeled validation data; no automatic weight optimization","TTA with many augmentations can exceed GPU memory; requires careful batch management","No built-in support for weighted ensemble training (e.g., knowledge distillation from ensemble)"],"requires":["Python 3.7+","PyTorch 1.9+","Multiple pre-trained models loaded in memory"],"input_types":["list of models (nn.Module instances)","input image or batch","ensemble strategy ('average', 'vote', 'learned_weight')"],"output_types":["ensemble prediction (tensor B, num_classes)","per-model predictions (dict or list)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_7","uri":"capability://image.visual.model.interpretability.and.visualization.utilities","name":"model interpretability and visualization utilities","description":"Provides tools for visualizing model predictions, attention maps (for Vision Transformers), and feature activations. Includes gradient-based visualization (Grad-CAM, saliency maps) and attention rollout for understanding which image regions influence predictions. Integrates with timm's model registry to automatically extract attention layers from Vision Transformers and other attention-based architectures.","intents":["I want to visualize which regions of an image a model attends to (attention maps for ViT)","I need to generate Grad-CAM visualizations to understand CNN predictions","I want to extract and visualize intermediate feature maps from a model"],"best_for":["researchers debugging model failures and understanding predictions","practitioners building explainability reports for stakeholders","teams validating that models learn meaningful features (not shortcuts)"],"limitations":["Attention visualization (attention rollout) is approximate and may not reflect true model reasoning","Grad-CAM is gradient-based and can be noisy for some architectures","No built-in support for concept-based explanations (e.g., TCAV)","Visualization quality depends on model architecture; some custom models may not be supported"],"requires":["Python 3.7+","PyTorch 1.9+","matplotlib or other visualization library","Optional: opencv for advanced image processing"],"input_types":["timm model (nn.Module)","input image (tensor or PIL)","visualization method ('grad_cam', 'attention_rollout', 'saliency')"],"output_types":["visualization image (numpy array or PIL Image)","attention weights (tensor)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_8","uri":"capability://automation.workflow.distributed.training.with.multi.gpu.and.multi.node.support","name":"distributed training with multi-gpu and multi-node support","description":"Provides utilities for distributed training across multiple GPUs (single machine) and multiple nodes (cluster) using PyTorch's DistributedDataParallel (DDP) and automatic mixed precision (AMP). Handles synchronization of batch normalization statistics across devices, gradient accumulation for effective larger batch sizes, and automatic learning rate scaling based on world size. Includes utilities for distributed checkpoint saving and resuming.","intents":["I want to train a model on multiple GPUs without manually managing DDP setup","I need to scale training to a cluster while maintaining convergence (automatic LR scaling)","I want to use mixed precision training to reduce memory and speed up training"],"best_for":["ML engineers training large models on multi-GPU or multi-node clusters","teams optimizing training time and resource utilization","researchers experimenting with large batch sizes and distributed training"],"limitations":["DDP setup requires careful handling of random seeds and data shuffling; easy to introduce subtle bugs","Synchronization overhead increases with number of nodes; diminishing returns beyond ~8 nodes","Mixed precision training may reduce accuracy for some models; requires validation","No built-in support for model parallelism (sharding) for very large models"],"requires":["Python 3.7+","PyTorch 1.9+","CUDA 11.0+ and NCCL for multi-GPU","Optional: Apex for advanced mixed precision"],"input_types":["model (nn.Module)","training dataset (DataLoader)","distributed config (world_size, rank, backend)"],"output_types":["trained model checkpoint","training logs (loss, accuracy per epoch)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-timm__cap_9","uri":"capability://data.processing.analysis.model.benchmarking.and.profiling.utilities","name":"model benchmarking and profiling utilities","description":"Provides tools for benchmarking model inference speed, memory usage, and FLOPs across different batch sizes, input resolutions, and hardware configurations. Includes profiling utilities to identify bottlenecks (compute-bound vs memory-bound), measure throughput (images/sec), and estimate latency percentiles. Integrates with PyTorch profiler to generate detailed performance traces and supports comparison across model families.","intents":["I want to measure inference latency and throughput for a model on my target hardware","I need to compare FLOPs and memory usage across different model architectures","I want to identify whether a model is compute-bound or memory-bound to guide optimization"],"best_for":["ML engineers selecting models for production based on latency/throughput constraints","teams optimizing inference performance","researchers benchmarking model families systematically"],"limitations":["Benchmarks are hardware-specific; results don't transfer across GPUs or CPUs","Profiling overhead can skew measurements; requires careful warmup and averaging","FLOPs estimates are theoretical and may not reflect actual hardware utilization","No built-in support for profiling on mobile devices or edge hardware"],"requires":["Python 3.7+","PyTorch 1.9+","Optional: NVIDIA Nsight for detailed GPU profiling"],"input_types":["model (nn.Module)","input shape (tuple)","batch sizes (list)","device ('cuda', 'cpu')"],"output_types":["benchmark results (dict): latency, throughput, memory, FLOPs","profiling trace (JSON or Chrome trace format)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+","Internet connection for initial weight download","~2-10GB GPU VRAM depending on model size","torchvision 0.10+","Pillow for image I/O","timm library","timm library installed","onnx and onnxruntime for ONNX export","Optional: TensorRT for GPU optimization"],"failure_modes":["Model registry is PyTorch-only; no TensorFlow or ONNX export built-in","Automatic weight caching requires ~50GB+ disk space for full model zoo","No built-in quantization or pruning — requires external tools for model compression","Inference speed varies significantly by architecture; no automatic hardware optimization (e.g., TensorRT)","Augmentation is CPU-bound; GPU-accelerated augmentation (e.g., Kornia) requires manual integration","Limited to 2D image transforms; no 3D medical imaging or video preprocessing","Model-specific preprocessing metadata must be manually maintained when new architectures are added","Custom models must follow timm's interface conventions; non-standard architectures may require manual adaptation","Registration is local to the Python process; no persistent model registry across projects","No built-in versioning or dependency management for custom models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.36,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:20.420Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-timm","compare_url":"https://unfragile.ai/compare?artifact=pypi-timm"}},"signature":"uMir212Qoy58y+OVN8V891SwK8p3CLH1gKWA8x+D7BUezgRRYUenK6r7TiG312fjivpo01fJ9QNWNSwtiLzbDA==","signedAt":"2026-06-15T18:20:21.679Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-timm","artifact":"https://unfragile.ai/pypi-timm","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-timm","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}