{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"detectron2","slug":"detectron2","name":"Detectron2","type":"repo","url":"https://github.com/facebookresearch/detectron2","page_url":"https://unfragile.ai/detectron2","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"detectron2__cap_0","uri":"capability://automation.workflow.yaml.based.hierarchical.configuration.system.with.lazy.evaluation","name":"yaml-based hierarchical configuration system with lazy evaluation","description":"Detectron2 implements a centralized CfgNode-based configuration system that uses YAML files to control all aspects of model training and inference. The system supports lazy configuration loading, allowing dynamic model instantiation without pre-defining all architecture choices. Configurations are hierarchically organized with defaults that can be overridden at runtime, enabling reproducible experiments and easy hyperparameter sweeps without code changes.","intents":["Define model architecture, training hyperparameters, and dataset paths in a single YAML file without modifying Python code","Run multiple experiments with different configurations by swapping YAML files","Share reproducible training recipes across teams with version-controlled configs","Override specific config values from command line for quick ablation studies"],"best_for":["Computer vision researchers running systematic ablation studies","Teams standardizing training pipelines across multiple projects","Practitioners who need reproducible, version-controlled experiment configurations"],"limitations":["YAML syntax can become verbose for deeply nested configurations with many model variants","Lazy configs require understanding of Python closures and deferred evaluation, adding cognitive overhead","No built-in config validation schema — invalid configs fail at runtime rather than parse time"],"requires":["Python 3.6+","PyYAML library","Understanding of Detectron2's config structure (CfgNode class)"],"input_types":["YAML files","Python dict objects","command-line string overrides"],"output_types":["CfgNode object","instantiated model and trainer objects"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_1","uri":"capability://image.visual.modular.backbone.head.architecture.with.pluggable.feature.extractors","name":"modular backbone-head architecture with pluggable feature extractors","description":"Detectron2 decomposes detection models into interchangeable backbone networks (ResNet, Vision Transformer, etc.) and task-specific heads (ROI heads for instance segmentation, keypoint detection heads). The architecture uses a registry pattern to dynamically instantiate backbones and heads from config, enabling researchers to swap components without rewriting model code. Backbones extract multi-scale features via FPN (Feature Pyramid Network), which are then consumed by heads that perform region-of-interest operations.","intents":["Experiment with different backbone architectures (ResNet50, ResNet101, ViT) without changing detection head code","Combine custom backbone implementations with pre-built heads for rapid prototyping","Add new detection heads (e.g., panoptic segmentation) while reusing existing backbone infrastructure","Benchmark backbone performance in isolation by swapping implementations in config"],"best_for":["Vision researchers developing new backbone architectures","Teams integrating state-of-the-art backbones (ViT, ConvNeXt) into existing detection pipelines","Practitioners building custom detection heads for domain-specific tasks"],"limitations":["Backbone-head interface assumes FPN-compatible feature outputs; custom backbones must implement specific output shapes","ROI head implementations are tightly coupled to specific proposal generation methods (RPN, ATSS), limiting flexibility","No automatic shape inference — mismatched backbone output channels and head input channels fail at runtime"],"requires":["PyTorch 1.8+","Understanding of FPN (Feature Pyramid Network) architecture","Familiarity with Detectron2's registry system (@BACKBONE_REGISTRY.register())"],"input_types":["raw image tensors (B, 3, H, W)","feature maps from backbone"],"output_types":["multi-scale feature pyramids","detection outputs (boxes, masks, keypoints)"],"categories":["image-visual","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_10","uri":"capability://image.visual.custom.model.architecture.implementation.via.modular.building.blocks","name":"custom model architecture implementation via modular building blocks","description":"Detectron2 enables custom model architecture implementation by composing modular building blocks: custom backbones (registered via @BACKBONE_REGISTRY), custom heads (registered via @ROI_HEADS_REGISTRY), and custom meta-architectures (GeneralizedRCNN, RetinaNet). The framework provides base classes (Backbone, ROIHeads) with standard interfaces, allowing new architectures to integrate seamlessly with existing training and evaluation code. Custom architectures inherit from nn.Module and implement forward() to accept standardized input format (list[dict]).","intents":["Implement a novel backbone architecture (e.g., Vision Transformer, ConvNeXt) and integrate it with existing detection heads","Design a custom detection head for a specialized task (panoptic segmentation, 3D object detection) using existing backbone infrastructure","Create a new meta-architecture (e.g., one-stage detector) by implementing the GeneralizedRCNN interface","Experiment with architecture variants (different normalization, activation functions) without forking the framework"],"best_for":["Computer vision researchers developing novel architectures","Teams adapting Detectron2 for specialized tasks (3D detection, panoptic segmentation, domain-specific detection)","Practitioners integrating cutting-edge backbones (ViT, ConvNeXt, EfficientNet) into detection pipelines"],"limitations":["Custom architectures must conform to Detectron2's input/output contracts (list[dict] input, Instances output) — non-standard interfaces require wrapper code","Debugging custom architectures requires understanding the full training pipeline — errors in forward() may manifest as downstream training failures","No automatic shape inference — mismatched tensor shapes between components fail at runtime","Custom architectures must be registered via decorators (@BACKBONE_REGISTRY.register()) — easy to forget registration and cause import errors"],"requires":["PyTorch 1.8+","Deep understanding of Detectron2's architecture (Backbone, ROIHeads, GeneralizedRCNN)","Familiarity with nn.Module and PyTorch model implementation","Knowledge of detection pipeline (FPN, RPN, ROI pooling)"],"input_types":["list[dict] with keys: image, height, width, instances (training only)"],"output_types":["Instances object with fields: pred_boxes, pred_classes, scores, pred_masks (optional)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_11","uri":"capability://automation.workflow.distributed.training.with.automatic.gradient.synchronization.and.loss.scaling","name":"distributed training with automatic gradient synchronization and loss scaling","description":"Detectron2 supports distributed training via torch.nn.parallel.DistributedDataParallel (DDP) with automatic gradient synchronization across GPUs/nodes. The training system handles distributed data loading (DistributedSampler for proper shuffling), gradient accumulation, and loss scaling for mixed-precision training. The trainer automatically detects the number of GPUs and distributes batches across processes, with rank-aware logging to avoid duplicate output.","intents":["Train models on multiple GPUs (single machine or multi-node) with automatic batch distribution and gradient synchronization","Scale training to large batches across multiple nodes without manual gradient averaging","Use mixed-precision training (FP16) with automatic loss scaling to reduce memory usage and training time","Monitor training progress across distributed processes with rank-aware logging"],"best_for":["Teams training large models (ResNet101, ViT) that require multi-GPU training","Practitioners with access to multi-node clusters (HPC, cloud) who need to scale training","Researchers studying the effect of batch size and learning rate on model convergence"],"limitations":["Distributed training requires NCCL backend and CUDA-capable GPUs — CPU-only distributed training is not supported","Synchronization overhead increases with number of processes — diminishing returns beyond 8 GPUs on single machine","Custom hooks must be rank-aware (check dist.get_rank()) to avoid duplicate logging and checkpointing","Debugging distributed training is difficult — errors in one process may not propagate to others, causing hangs"],"requires":["PyTorch 1.8+ with NCCL support","CUDA-capable GPUs (1 GPU minimum, 2+ for distributed training)","NCCL library installed and properly configured","torch.distributed initialization (usually handled by Detectron2 trainer)"],"input_types":["training config with num_gpus setting","model and optimizer","training data"],"output_types":["trained model checkpoints","training logs from rank 0 process"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_12","uri":"capability://image.visual.instance.segmentation.with.mask.prediction.and.mask.level.metrics","name":"instance segmentation with mask prediction and mask-level metrics","description":"Detectron2 implements instance segmentation via Mask R-CNN, which extends Faster R-CNN with a mask prediction head that generates per-instance segmentation masks. The mask head operates on RoI-aligned features and predicts binary masks via FCN (Fully Convolutional Network) architecture. Evaluation includes mask-level metrics (mask IoU, mask AP) computed via COCO evaluation code, enabling precise assessment of segmentation quality beyond bounding box accuracy.","intents":["Predict instance-level segmentation masks for each detected object in an image","Evaluate segmentation quality using standard metrics (mask AP, mask IoU) on COCO or custom datasets","Fine-tune pre-trained Mask R-CNN models on custom datasets with instance mask annotations","Extract per-instance masks for downstream processing (object tracking, 3D reconstruction)"],"best_for":["Practitioners requiring precise object boundaries (medical imaging, document analysis, autonomous driving)","Teams with instance-level mask annotations who want to leverage segmentation information","Researchers studying the trade-off between detection and segmentation accuracy"],"limitations":["Mask prediction adds computational overhead (~20-30% slower than Faster R-CNN) and memory usage","Mask quality depends on RPN proposal quality — poor proposals lead to poor masks even with good mask head","Mask annotations are expensive to collect — requires pixel-level labeling vs bounding box labeling","Mask evaluation (COCO mask AP) is slow — evaluation time scales with number of instances"],"requires":["PyTorch 1.8+","Instance mask annotations in COCO JSON format (RLE-encoded or polygon format)","CUDA-capable GPU for efficient mask prediction","pycocotools for mask evaluation"],"input_types":["images","instance mask annotations (RLE or polygon format)"],"output_types":["predicted masks (binary or soft masks)","mask AP and mask IoU metrics"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_13","uri":"capability://image.visual.keypoint.detection.with.multi.person.pose.estimation","name":"keypoint detection with multi-person pose estimation","description":"Detectron2 supports keypoint detection via KeypointRCNNHead, which predicts keypoint locations (e.g., human joints) for each detected instance. The keypoint head operates on RoI-aligned features and outputs heatmaps for each keypoint, which are post-processed to extract coordinates. Evaluation includes keypoint-level metrics (keypoint AP, OKS) computed via COCO evaluation, enabling assessment of pose estimation accuracy. The framework supports multi-person pose estimation by detecting person instances and predicting keypoints for each.","intents":["Predict human keypoints (joints, landmarks) for each detected person in an image","Evaluate pose estimation quality using standard metrics (keypoint AP, OKS) on COCO or custom datasets","Fine-tune pre-trained keypoint detection models on custom pose datasets","Extract keypoint coordinates for downstream applications (action recognition, motion capture)"],"best_for":["Practitioners building pose estimation systems (fitness tracking, sports analytics, motion capture)","Teams with keypoint annotations who want to leverage pose information","Researchers studying multi-person pose estimation and keypoint localization accuracy"],"limitations":["Keypoint prediction adds computational overhead (~10-15% slower than Faster R-CNN)","Keypoint accuracy depends on detection quality — missed or misaligned detections lead to poor keypoints","Keypoint annotations are expensive to collect — requires precise landmark labeling","Heatmap post-processing (argmax or soft-argmax) introduces quantization error — sub-pixel accuracy requires interpolation"],"requires":["PyTorch 1.8+","Keypoint annotations in COCO format (x, y, visibility per keypoint)","CUDA-capable GPU for efficient keypoint prediction","pycocotools for keypoint evaluation"],"input_types":["images","keypoint annotations (x, y, visibility per instance)"],"output_types":["predicted keypoint coordinates and confidence scores","keypoint AP and OKS metrics"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_14","uri":"capability://code.generation.editing.custom.model.architecture.composition.via.modular.components","name":"custom model architecture composition via modular components","description":"Detectron2 enables custom architecture implementation by composing modular components: custom backbones (registered in BACKBONE_REGISTRY), custom heads (registered in ROI_HEADS_REGISTRY), and custom proposal generators. Developers implement nn.Module subclasses and register them, then reference them in configs. The framework handles component instantiation and wiring, enabling complex architectures without modifying core Detectron2 code.","intents":["I want to implement a custom backbone architecture and use it in detection models","I need to add a custom head for a novel detection task (e.g., 3D object detection)","I want to combine multiple backbones or heads in a single model","I need to implement a research paper's architecture without forking Detectron2"],"best_for":["researchers implementing novel detection architectures","teams extending Detectron2 for custom tasks","practitioners adapting Detectron2 to domain-specific problems"],"limitations":["Custom components must follow Detectron2's interface conventions (input/output shapes, field names)","Registry-based composition can be opaque — debugging component interactions is hard","No automatic validation of component compatibility — mismatched components fail at runtime","Custom components require understanding Detectron2's data flow and conventions"],"requires":["PyTorch 1.8+","Understanding of Detectron2's registry system and component interfaces","Knowledge of detection architecture design"],"input_types":["custom nn.Module implementations","config file referencing custom components"],"output_types":["instantiated custom model","trained weights"],"categories":["code-generation-editing","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_2","uri":"capability://data.processing.analysis.dataset.registration.and.catalog.system.with.automatic.coco.custom.dataset.loading","name":"dataset registration and catalog system with automatic coco/custom dataset loading","description":"Detectron2 provides a dataset registry that decouples dataset definitions from model code via the DatasetCatalog class. Datasets are registered with metadata (image paths, annotation formats) and automatically loaded on-demand during training. The system includes built-in loaders for COCO, Pascal VOC, and custom formats, with a DataLoader abstraction that handles batching, sampling, and augmentation. Custom datasets are registered via simple Python functions that return list[dict] with standardized keys (image, annotations, height, width).","intents":["Register custom datasets without modifying Detectron2 source code by adding a registration function","Switch between datasets (COCO, custom proprietary data) by changing a single config line","Load datasets with automatic train/val/test splits and class-aware sampling strategies","Integrate proprietary annotation formats by implementing a custom dataset loader function"],"best_for":["Teams with custom domain-specific datasets (medical imaging, satellite imagery, industrial inspection)","Researchers comparing models across multiple datasets without dataset-specific preprocessing code","Practitioners migrating from other frameworks (YOLO, MMDetection) with existing annotation formats"],"limitations":["Dataset registration requires Python code — no declarative YAML-only dataset definition","Built-in loaders assume specific annotation structures (COCO JSON format); non-standard formats require custom loader implementation","No built-in data versioning or integrity checking — dataset changes are not tracked automatically","Memory overhead for large datasets because DatasetCatalog loads all metadata into memory at initialization"],"requires":["Python 3.6+","PyTorch DataLoader","COCO API (pycocotools) for COCO datasets","Annotation files in COCO JSON or custom format"],"input_types":["COCO JSON annotation files","custom Python dataset loader functions","image directories"],"output_types":["list[dict] with keys: image, annotations, height, width","batched tensors via DataLoader"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_3","uri":"capability://image.visual.multi.scale.feature.pyramid.generation.with.fpn.and.proposal.based.region.extraction","name":"multi-scale feature pyramid generation with fpn and proposal-based region extraction","description":"Detectron2 implements Feature Pyramid Networks (FPN) that generate multi-scale feature maps from backbone outputs, enabling detection of objects at different scales. The RPN (Region Proposal Network) generates region proposals from these pyramids, which are then extracted via ROI pooling/alignment operations (RoIAlign for precise alignment, RoIPool for speed). This two-stage pipeline separates proposal generation from classification, enabling flexible head architectures (Mask R-CNN, Cascade R-CNN) that operate on extracted regions.","intents":["Detect objects across multiple scales (small pedestrians, large vehicles) using a single model","Generate region proposals efficiently from multi-scale features without exhaustive sliding windows","Extract precise region features using RoIAlign for downstream classification and mask prediction","Implement custom proposal generation strategies (ATSS, FCOS) by replacing RPN while reusing FPN infrastructure"],"best_for":["Practitioners building two-stage detectors (Faster R-CNN, Mask R-CNN, Cascade R-CNN)","Researchers experimenting with proposal generation methods (RPN variants, anchor-free approaches)","Teams requiring precise object localization where RoIAlign's sub-pixel accuracy matters (medical imaging, document analysis)"],"limitations":["FPN adds ~15-20% computational overhead compared to single-scale backbones","RPN requires careful anchor configuration (scales, aspect ratios) — poor anchor design significantly degrades proposal quality","RoIAlign requires interpolation which adds ~5-10ms latency per image; RoIPool is faster but less accurate","Proposal extraction is memory-intensive for large feature maps — batch size must be reduced for high-resolution inputs"],"requires":["PyTorch 1.8+","CUDA-capable GPU for efficient RoIAlign operations","Understanding of anchor-based proposal generation (RPN)","Torchvision ops (roi_align, roi_pool) or custom CUDA implementations"],"input_types":["multi-scale feature maps from backbone","image metadata (height, width, scale)"],"output_types":["region proposals (boxes, scores)","extracted region features (pooled tensors)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_4","uri":"capability://automation.workflow.flexible.training.loop.with.hook.based.event.system.for.custom.callbacks","name":"flexible training loop with hook-based event system for custom callbacks","description":"Detectron2's training system is built around TrainerBase with a hook-based event system that fires callbacks at specific training stages (before/after epoch, before/after iteration, before/after training). Hooks implement standard interfaces (before_train, after_step, etc.) and are registered with the trainer, enabling custom logging, checkpointing, learning rate scheduling, and validation without modifying core training code. The system supports distributed training via DistributedDataParallel with automatic gradient synchronization and loss scaling.","intents":["Add custom validation logic (e.g., compute mAP on custom test set) without modifying training loop","Implement custom learning rate schedules (warmup, cosine annealing, step decay) via LRScheduler hooks","Log metrics to external systems (Weights & Biases, TensorBoard) by implementing custom hooks","Save checkpoints at specific intervals or based on validation metrics via CheckpointerHook"],"best_for":["Researchers implementing custom training strategies (curriculum learning, hard example mining)","Teams integrating Detectron2 with MLOps platforms (Weights & Biases, Neptune, Kubeflow)","Practitioners requiring fine-grained control over training dynamics without forking the framework"],"limitations":["Hook execution order is implicit — debugging hook interactions requires understanding the full training loop","Distributed training requires careful synchronization of hooks across processes; some hooks (logging) must be guarded with rank checks","No built-in support for mixed-precision training (AMP) — requires manual gradient scaling or third-party libraries","Hook state is not automatically checkpointed — custom hooks must implement state serialization for resumable training"],"requires":["PyTorch 1.8+","torch.nn.parallel.DistributedDataParallel for multi-GPU training","Understanding of PyTorch training loops and gradient computation","NCCL backend for distributed training (CUDA-capable GPUs)"],"input_types":["training config","model and optimizer","training data"],"output_types":["trained model checkpoints","training logs and metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_5","uri":"capability://data.processing.analysis.unified.evaluation.framework.with.pluggable.dataset.evaluators.and.metric.computation","name":"unified evaluation framework with pluggable dataset evaluators and metric computation","description":"Detectron2 provides a DatasetEvaluator interface that decouples metric computation from model evaluation. Evaluators implement process() to consume model outputs and accumulate statistics, then evaluate() to compute final metrics (mAP, mIoU, etc.). The framework includes built-in evaluators for COCO (COCOEvaluator), Pascal VOC (PascalVOCEvaluator), and custom datasets. Evaluators are composed via DatasetEvaluators which runs multiple evaluators in parallel and aggregates results, enabling simultaneous computation of detection, segmentation, and keypoint metrics.","intents":["Evaluate models on standard benchmarks (COCO, Pascal VOC) using official evaluation code without manual metric implementation","Implement custom metrics (domain-specific IoU, class-weighted mAP) by subclassing DatasetEvaluator","Compute multiple metrics simultaneously (detection + segmentation + keypoints) on the same model outputs","Integrate custom evaluation logic into training loop via EvalHook without modifying core evaluation code"],"best_for":["Researchers benchmarking on standard datasets (COCO, Pascal VOC, Cityscapes) with official metrics","Teams with custom evaluation requirements (domain-specific metrics, class imbalance handling)","Practitioners comparing models across multiple tasks (detection, segmentation, keypoints) in a single evaluation run"],"limitations":["Evaluators must implement process() and evaluate() separately — no automatic batching of outputs","COCO evaluation requires pycocotools which has platform-specific compilation issues on some systems","Evaluators accumulate all predictions in memory — evaluation fails for very large datasets that don't fit in RAM","No built-in support for streaming evaluation — all predictions must be collected before metrics are computed"],"requires":["PyTorch 1.8+","pycocotools for COCO evaluation","Annotation files in dataset-specific format (COCO JSON, Pascal VOC XML)","Understanding of DatasetEvaluator interface"],"input_types":["model predictions (boxes, masks, keypoints)","ground truth annotations"],"output_types":["metric dictionaries (mAP, mIoU, etc.)","per-class breakdowns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_6","uri":"capability://memory.knowledge.pre.trained.model.zoo.with.100.checkpoints.across.architectures.and.datasets","name":"pre-trained model zoo with 100+ checkpoints across architectures and datasets","description":"Detectron2 provides a model zoo (MODEL_ZOO.md) with pre-trained checkpoints for Mask R-CNN, Faster R-CNN, RetinaNet, Cascade R-CNN, and other architectures trained on COCO, Pascal VOC, and Cityscapes. Models are organized by backbone (ResNet50, ResNet101, ViT) and task (detection, instance segmentation, keypoint detection). The ModelZoo API enables one-line model loading with automatic checkpoint downloading and caching, eliminating manual weight management.","intents":["Load a pre-trained Mask R-CNN model with a single API call for immediate inference on custom images","Fine-tune pre-trained backbones on custom datasets without training from scratch","Compare multiple pre-trained architectures (Faster R-CNN vs Cascade R-CNN) on the same dataset","Access training recipes and hyperparameters used to train pre-trained models for reproducibility"],"best_for":["Practitioners building production systems who need immediate inference capability","Teams with limited compute budgets who benefit from transfer learning","Researchers benchmarking custom methods against pre-trained baselines"],"limitations":["Pre-trained models are trained on COCO/Pascal VOC — domain shift may require fine-tuning for specialized domains (medical, satellite imagery)","Model zoo is static — new architectures or training recipes require manual checkpoint creation","Checkpoint downloads are large (100-500MB) — slow on bandwidth-limited connections","No automatic model versioning — updating to newer checkpoints requires manual URL changes"],"requires":["PyTorch 1.8+","Internet connection for initial checkpoint download","Disk space for cached checkpoints (~5GB for full model zoo)","CUDA-capable GPU for inference (CPU inference is very slow)"],"input_types":["model name string (e.g., 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x')"],"output_types":["loaded PyTorch model with pre-trained weights"],"categories":["memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_7","uri":"capability://automation.workflow.multi.format.model.export.for.deployment.torchscript.onnx.caffe2","name":"multi-format model export for deployment (torchscript, onnx, caffe2)","description":"Detectron2 supports exporting trained models to multiple deployment formats: TorchScript (for PyTorch inference servers), ONNX (for cross-framework compatibility), and Caffe2 (for mobile/edge deployment). The export pipeline includes model tracing/scripting, input/output shape inference, and format-specific optimizations. Exported models can be deployed without Detectron2 dependencies, enabling integration with production inference systems (TensorRT, ONNX Runtime, Caffe2).","intents":["Export a trained Mask R-CNN model to TorchScript for deployment in PyTorch inference servers","Convert models to ONNX format for inference on non-PyTorch platforms (C++, Java, JavaScript)","Deploy models on mobile devices via Caffe2 export with reduced model size and latency","Integrate exported models with production inference frameworks (TensorRT for GPU, ONNX Runtime for CPU)"],"best_for":["Teams deploying models to production inference servers (TorchServe, Triton)","Practitioners requiring cross-platform inference (CPU, GPU, mobile, edge devices)","Organizations with strict dependency constraints that cannot include full PyTorch/Detectron2 in production"],"limitations":["TorchScript export requires tracing or scripting — dynamic control flow (if statements, loops) may not export correctly","ONNX export loses some PyTorch-specific optimizations — exported models may be slower than native PyTorch","Caffe2 export is deprecated and no longer actively maintained — limited support for newer architectures","Post-processing (NMS, score filtering) must be implemented separately in deployment code — exported models output raw predictions","Exported models are not backward compatible — model format changes require re-exporting all checkpoints"],"requires":["PyTorch 1.8+","onnx and onnxruntime for ONNX export","Caffe2 (optional, for Caffe2 export)","Understanding of model tracing vs scripting trade-offs"],"input_types":["trained PyTorch model","sample input tensors for tracing"],"output_types":["TorchScript (.pt), ONNX (.onnx), or Caffe2 (.pb) model files"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_8","uri":"capability://data.processing.analysis.data.augmentation.pipeline.with.geometric.and.photometric.transformations","name":"data augmentation pipeline with geometric and photometric transformations","description":"Detectron2 provides a composable augmentation system (detectron2/data/transforms) that applies geometric (rotation, flipping, cropping) and photometric (brightness, contrast, saturation) transformations to images and annotations. Augmentations are defined declaratively in config and applied via the Augmentation class hierarchy, which handles coordinate transformation for bounding boxes and segmentation masks. The pipeline supports custom augmentations by subclassing Augmentation and implementing the __call__ method.","intents":["Apply standard augmentations (random flip, crop, rotation) to training data without manual implementation","Compose multiple augmentations in a pipeline defined in config without code changes","Implement domain-specific augmentations (e.g., weather effects for autonomous driving) by subclassing Augmentation","Ensure augmentations correctly transform both images and annotations (boxes, masks) without manual coordinate updates"],"best_for":["Practitioners training detection models on small datasets where augmentation is critical for generalization","Teams with domain-specific augmentation requirements (medical imaging, satellite imagery, industrial inspection)","Researchers studying the effect of augmentation strategies on model performance"],"limitations":["Augmentation pipeline is applied during data loading — no offline augmentation caching, increasing training time","Custom augmentations require understanding of coordinate transformation for boxes and masks","Some augmentations (e.g., elastic deformation) are not included — users must implement custom augmentations","Augmentation parameters are fixed per config — no adaptive augmentation strategies (AutoAugment, RandAugment) built-in"],"requires":["PyTorch 1.8+","Pillow for image operations","Understanding of coordinate transformation for bounding boxes and masks","Familiarity with Detectron2's Augmentation class interface"],"input_types":["images (PIL Image or numpy array)","annotations (boxes, masks, keypoints)"],"output_types":["augmented images and transformed annotations"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__cap_9","uri":"capability://image.visual.visualization.utilities.for.model.predictions.and.dataset.exploration","name":"visualization utilities for model predictions and dataset exploration","description":"Detectron2 provides visualization tools (Visualizer class) that overlay model predictions (boxes, masks, keypoints) on images for debugging and analysis. The visualizer supports custom color schemes, confidence thresholds, and per-class visualization. Built-in utilities enable dataset exploration (visualizing annotations), prediction analysis (comparing predictions across models), and error analysis (identifying failure cases). Visualizations can be saved as images or displayed interactively.","intents":["Visualize model predictions on test images to qualitatively assess detection/segmentation quality","Explore dataset annotations to identify labeling errors or class imbalances","Compare predictions across multiple models on the same image for model selection","Analyze failure cases by visualizing predictions with low confidence or high IoU errors"],"best_for":["Practitioners debugging model failures and understanding prediction quality","Teams conducting dataset quality audits and identifying labeling errors","Researchers analyzing model behavior and comparing architectures qualitatively"],"limitations":["Visualizer is CPU-based — rendering large batches of images is slow","No built-in interactive visualization tools (e.g., web-based annotation interface)","Visualization output is static images — no video or temporal analysis tools","Custom visualization logic requires subclassing Visualizer — limited extensibility for domain-specific visualizations"],"requires":["PyTorch 1.8+","Pillow for image operations","Matplotlib for interactive visualization (optional)","Model predictions in Detectron2 format (Instances object)"],"input_types":["images (numpy array or PIL Image)","model predictions (Instances object)","ground truth annotations"],"output_types":["annotated images (numpy array or saved PNG/JPG files)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"detectron2__headline","uri":"capability://image.visual.modular.object.detection.and.segmentation.framework","name":"modular object detection and segmentation framework","description":"Detectron2 is a modular object detection and segmentation framework built on PyTorch, offering state-of-the-art algorithms like Mask R-CNN and Cascade R-CNN for computer vision tasks.","intents":["best object detection framework","object detection for image segmentation","top frameworks for computer vision","best PyTorch segmentation library","object detection solutions for research"],"best_for":["research applications","real-time inference"],"limitations":["requires PyTorch knowledge"],"requires":["Python","PyTorch"],"input_types":["images","videos"],"output_types":["detected objects","segmented images"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","PyYAML library","Understanding of Detectron2's config structure (CfgNode class)","PyTorch 1.8+","Understanding of FPN (Feature Pyramid Network) architecture","Familiarity with Detectron2's registry system (@BACKBONE_REGISTRY.register())","Deep understanding of Detectron2's architecture (Backbone, ROIHeads, GeneralizedRCNN)","Familiarity with nn.Module and PyTorch model implementation","Knowledge of detection pipeline (FPN, RPN, ROI pooling)","PyTorch 1.8+ with NCCL support"],"failure_modes":["YAML syntax can become verbose for deeply nested configurations with many model variants","Lazy configs require understanding of Python closures and deferred evaluation, adding cognitive overhead","No built-in config validation schema — invalid configs fail at runtime rather than parse time","Backbone-head interface assumes FPN-compatible feature outputs; 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