{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mmdetection","slug":"mmdetection","name":"MMDetection","type":"repo","url":"https://github.com/open-mmlab/mmdetection","page_url":"https://unfragile.ai/mmdetection","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mmdetection__cap_0","uri":"capability://code.generation.editing.modular.detector.composition.via.registry.based.architecture","name":"modular detector composition via registry-based architecture","description":"Constructs object detection models by composing independent modules (backbone, neck, head, loss) registered in a centralized registry system. Each module type (ResNet, FPN, RetinaNet head, Focal Loss) is independently registered and instantiated via configuration, enabling researchers to mix-and-match components without code modification. The registry pattern decouples module implementation from the detector assembly logic, allowing new architectures to be added by simply registering new components.","intents":["I want to build a custom detector by combining a Swin Transformer backbone with a PAFPN neck and a custom detection head without modifying framework code","I need to experiment with different loss functions (Focal Loss vs IoU Loss) by swapping them in configuration","I want to add a new backbone architecture and have it automatically available to all detector types"],"best_for":["computer vision researchers prototyping novel detector architectures","teams building production detection systems with custom components","practitioners needing rapid experimentation with architecture variants"],"limitations":["Registry-based instantiation adds ~5-10ms overhead per model initialization due to dynamic class lookup","Requires understanding of MMDetection's config schema and module interfaces; steep learning curve for newcomers","Custom modules must inherit from base classes and implement required methods (_forward_train, _forward_test) or registration fails silently"],"requires":["PyTorch 1.9+","Python 3.7+","mmcv library (OpenMMLab's core utilities)","Understanding of detector component interfaces (BaseDetector, BaseHead, BaseBackbone)"],"input_types":["Python configuration files (.py)","Module class definitions inheriting from MMDetection base classes"],"output_types":["Instantiated PyTorch nn.Module detector objects","Registered module classes available for composition"],"categories":["code-generation-editing","architecture-composition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_1","uri":"capability://automation.workflow.configuration.driven.training.pipeline.with.distributed.support","name":"configuration-driven training pipeline with distributed support","description":"Defines complete training workflows (data loading, augmentation, optimization, validation) through Python configuration files that are parsed and executed by MMDetection's training engine. The pipeline supports distributed training across multiple GPUs/nodes via PyTorch DistributedDataParallel, automatic mixed precision (AMP), gradient accumulation, and learning rate scheduling. Config files specify dataset paths, augmentation transforms, optimizer settings, and checkpoint intervals, which the training loop executes without requiring code changes.","intents":["I want to train a detector on a custom dataset by modifying only the config file, not the training code","I need to scale training across 8 GPUs with synchronized batch normalization and gradient accumulation","I want to apply test-time augmentation (TTA) during validation to improve mAP without rewriting inference logic"],"best_for":["ML engineers training production detection models at scale","researchers comparing detector architectures with controlled hyperparameters","teams with limited PyTorch expertise who need reproducible training workflows"],"limitations":["Config-based approach obscures control flow; debugging training issues requires understanding config parsing and the training loop implementation","Distributed training requires careful synchronization of batch statistics; incorrect config can cause gradient mismatch across processes","No built-in support for dynamic learning rate scheduling based on validation metrics (e.g., ReduceLROnPlateau); requires custom callbacks"],"requires":["PyTorch 1.9+ with CUDA support for distributed training","NCCL backend for multi-GPU synchronization","mmcv library with training utilities","Properly formatted dataset annotations (COCO JSON, Pascal VOC XML, or custom format)"],"input_types":["Python config files specifying model, data, optimizer, schedule","Image datasets with bounding box annotations","Pre-trained model checkpoints (.pth files)"],"output_types":["Trained model checkpoints saved at intervals","Training logs with loss curves and validation metrics","Evaluation results (mAP, mAP@50, per-class metrics)"],"categories":["automation-workflow","training-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_10","uri":"capability://tool.use.integration.inference.api.with.batch.processing.and.model.deployment","name":"inference api with batch processing and model deployment","description":"Provides a unified inference interface (inference_detector function) that loads a trained model from checkpoint, preprocesses images, runs inference, and postprocesses predictions. The API supports batch inference (multiple images at once), test-time augmentation (TTA), and model deployment via ONNX export or TensorRT optimization. Inference can run on CPU or GPU; batch size is automatically adjusted based on available memory. The modular design allows custom preprocessing/postprocessing without modifying the core inference loop.","intents":["I want to load a trained detector and run inference on new images without writing boilerplate code","I need to batch-process multiple images efficiently for throughput optimization","I want to deploy a detector to production with ONNX export or TensorRT optimization for low-latency inference"],"best_for":["practitioners deploying trained detectors to production","teams building inference pipelines for batch processing","applications requiring low-latency inference (video processing, real-time detection)"],"limitations":["Batch inference requires images to be resized to the same size; variable-size images require padding or multiple forward passes","Test-time augmentation (TTA) increases inference latency by 3-5x (5-10 augmented versions per image); useful for accuracy but impractical for real-time applications","ONNX export requires careful handling of custom operations; some MMDetection components (deformable convolution, rotated NMS) may not export cleanly","TensorRT optimization is NVIDIA-specific; requires CUDA and TensorRT installation; not portable across hardware"],"requires":["PyTorch 1.9+","Trained model checkpoint (.pth file)","Model config file (.py) specifying architecture","Optional: ONNX or TensorRT for deployment"],"input_types":["Images (JPEG, PNG, or numpy arrays)","Batch of images (for batch inference)","Optional: augmentation parameters (for TTA)"],"output_types":["Detected bounding boxes with confidence scores","Class predictions per detection","Optional: instance masks (if model supports segmentation)","ONNX model file (for deployment)"],"categories":["tool-use-integration","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_11","uri":"capability://image.visual.visualization.and.analysis.tools.for.detection.results.and.model.behavior","name":"visualization and analysis tools for detection results and model behavior","description":"Provides utilities for visualizing detection results (bounding boxes, masks, keypoints overlaid on images), analyzing model behavior (attention maps, feature visualizations), and debugging predictions. Tools include image_demo.py for single-image inference with visualization, batch visualization for multiple images, and analysis tools for computing per-class metrics, false positive analysis, and confusion matrices. Visualizations are saved as images or videos for easy inspection.","intents":["I want to visualize detection results on test images to qualitatively assess model performance","I need to debug why my detector is failing on certain images (false positives, missed detections)","I want to analyze per-class performance and identify which classes are hardest to detect"],"best_for":["practitioners debugging detector failures and improving performance","researchers analyzing model behavior and attention patterns","teams creating visualizations for reports and presentations"],"limitations":["Visualization tools are primarily for debugging; not optimized for large-scale analysis of thousands of images","Attention map visualization requires understanding of transformer architectures; not applicable to CNN-based detectors","False positive analysis requires manual inspection; no automated categorization of failure modes","Video visualization can be memory-intensive for long videos; requires careful frame sampling"],"requires":["PyTorch 1.9+","OpenCV or Pillow for image manipulation","Matplotlib for plotting","Trained model checkpoint and config"],"input_types":["Images or videos","Detection predictions (bounding boxes, confidence scores, class labels)","Ground truth annotations (optional, for comparison)"],"output_types":["Visualized images with bounding boxes overlaid","Videos with detection results","Analysis plots (per-class AP, confusion matrices)","Attention maps (for transformer-based detectors)"],"categories":["image-visual","analysis-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_12","uri":"capability://automation.workflow.semi.supervised.and.self.supervised.learning.with.pseudo.labeling","name":"semi-supervised and self-supervised learning with pseudo-labeling","description":"Implements semi-supervised detection where unlabeled data is leveraged through pseudo-labeling: a teacher model generates pseudo-labels on unlabeled data, which are used to train a student model. The system supports confidence thresholding to filter low-quality pseudo-labels, exponential moving average (EMA) teacher updates for stability, and consistency regularization between student and augmented student predictions. Self-supervised pre-training (e.g., MoCo, SimCLR) can be used to initialize the backbone before supervised fine-tuning.","intents":["I have a large unlabeled dataset and want to leverage it to improve detector performance","I want to pre-train a detector backbone using self-supervised learning before fine-tuning on labeled data","I need to improve detector performance when labeled data is scarce or expensive to obtain"],"best_for":["practitioners with limited labeled data but access to large unlabeled datasets","research on semi-supervised and self-supervised detection","applications where annotation is expensive (medical imaging, specialized domains)"],"limitations":["Pseudo-labeling quality is critical; low-quality pseudo-labels degrade performance; requires careful confidence thresholding and teacher model selection","Semi-supervised training is unstable; requires careful hyperparameter tuning (EMA decay, confidence threshold, consistency weight)","Computational cost is high; requires training both teacher and student models; typically 2-3x slower than supervised training","Pseudo-label bias can accumulate; teacher model errors are propagated to student, potentially degrading performance"],"requires":["PyTorch 1.9+","Large unlabeled dataset","Pre-trained teacher model or self-supervised pre-training code","Careful hyperparameter tuning (EMA decay, confidence threshold)"],"input_types":["Labeled dataset (for supervised training)","Unlabeled dataset (for pseudo-labeling)","Optional: pre-trained backbone weights"],"output_types":["Trained student model","Pseudo-labels on unlabeled data","Performance metrics on labeled validation set"],"categories":["automation-workflow","semi-supervised-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_13","uri":"capability://image.visual.model.analysis.and.visualization.tools.for.debugging","name":"model analysis and visualization tools for debugging","description":"MMDetection provides analysis tools for understanding detector behavior: feature map visualization (showing what features the model learns), attention map visualization (for transformer-based detectors), prediction analysis (false positives, false negatives, localization errors), and dataset statistics. These tools help practitioners debug poor performance by identifying failure modes (e.g., small object detection failures, class confusion).","intents":["I want to visualize what features my detector learns to understand its decision-making","I need to analyze failure modes (false positives, false negatives) to improve my dataset or model","I want to understand which object classes are confused by my detector"],"best_for":["practitioners debugging detector failures and improving performance","researchers understanding learned representations in detection models","teams analyzing dataset quality and annotation errors"],"limitations":["Feature visualization is computationally expensive — requires forward passes through intermediate layers","Attention visualization for transformers is complex — multiple attention heads make interpretation difficult","Analysis tools are primarily for offline debugging — not suitable for real-time monitoring"],"requires":["Python 3.7+","PyTorch 1.6+","Matplotlib or other visualization library"],"input_types":["trained detector model","images and predictions"],"output_types":["feature map visualizations","attention map visualizations","failure mode analysis reports","dataset statistics"],"categories":["image-visual","analysis-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_2","uri":"capability://image.visual.multi.stage.detector.architecture.with.cascade.refinement","name":"multi-stage detector architecture with cascade refinement","description":"Implements two-stage detectors (Faster R-CNN, Cascade R-CNN, Mask R-CNN) that decompose detection into region proposal generation and region classification/refinement. The architecture uses a backbone for feature extraction, an RPN (Region Proposal Network) to generate candidate boxes, and ROI heads to classify and refine proposals. Cascade R-CNN extends this with multiple sequential refinement stages, each with its own classifier and bounding box regressor, progressively improving proposal quality. The modular design allows swapping backbone, RPN, and head components independently.","intents":["I need to detect objects with high localization accuracy using iterative bounding box refinement across multiple stages","I want to perform instance segmentation by adding a mask prediction head to a two-stage detector","I need to adapt a pre-trained Faster R-CNN to a new dataset by fine-tuning only the classification head"],"best_for":["applications requiring high-precision object localization (medical imaging, autonomous driving)","teams building instance segmentation systems","practitioners with moderate computational budgets (two-stage detectors are slower than single-stage but more accurate)"],"limitations":["Two-stage detectors are 2-5x slower than single-stage detectors (YOLO, SSD) due to RPN proposal generation and per-proposal processing","Cascade R-CNN requires careful tuning of IoU thresholds across stages; suboptimal thresholds degrade performance significantly","ROI pooling/alignment operations add memory overhead; batch size is limited by GPU memory when processing many proposals"],"requires":["PyTorch 1.9+","mmcv library with ROI operations (roi_align, roi_pool)","Backbone model (ResNet, ResNeXt, Swin Transformer) with feature pyramid","Annotated dataset with bounding boxes (COCO, Pascal VOC format)"],"input_types":["Images (any resolution, typically resized to 800x1333 for training)","Ground truth bounding boxes with class labels","Optional instance masks for Mask R-CNN variant"],"output_types":["Detected bounding boxes with confidence scores","Class predictions per detection","Optional instance masks (Mask R-CNN)","Refined proposals from each cascade stage"],"categories":["image-visual","detection-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_3","uri":"capability://image.visual.single.stage.detector.with.anchor.free.and.anchor.based.variants","name":"single-stage detector with anchor-free and anchor-based variants","description":"Implements efficient single-stage detectors (RetinaNet, FCOS, ATSS) that predict bounding boxes and class scores directly from feature maps without generating region proposals. Anchor-based variants (RetinaNet, ATSS) use predefined anchor boxes at multiple scales and aspect ratios; anchor-free variants (FCOS, CenterNet) predict box offsets from feature map points directly. All variants use feature pyramids (FPN, PAFPN) to handle multi-scale objects. The modular design allows swapping detection heads while keeping the backbone and neck fixed.","intents":["I need a fast detector for real-time inference (video processing, edge devices) with minimal latency","I want to detect objects at multiple scales without manually tuning anchor aspect ratios","I need to deploy a detector on mobile/edge hardware with a small model footprint"],"best_for":["real-time detection applications (video surveillance, autonomous driving perception)","edge deployment scenarios with latency constraints","practitioners prioritizing inference speed over maximum accuracy"],"limitations":["Single-stage detectors sacrifice accuracy for speed; typically 2-5% lower mAP than two-stage detectors on COCO","Anchor-free detectors (FCOS) are sensitive to feature map resolution and require careful tuning of center-ness weighting","Class imbalance (background vs foreground) requires focal loss or hard negative mining; naive cross-entropy training fails","Anchor-based detectors require manual tuning of anchor scales and aspect ratios per dataset; suboptimal anchors significantly degrade performance"],"requires":["PyTorch 1.9+","mmcv library with NMS and anchor generation utilities","Feature pyramid network (FPN or PAFPN) for multi-scale detection","Focal loss implementation for handling class imbalance"],"input_types":["Images (typically 320x320 to 1333x1333 depending on speed/accuracy tradeoff)","Ground truth bounding boxes with class labels","Anchor definitions (scales, aspect ratios) for anchor-based variants"],"output_types":["Detected bounding boxes with confidence scores","Class predictions per detection","Feature map activations (for visualization/debugging)"],"categories":["image-visual","detection-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_4","uri":"capability://image.visual.transformer.based.detection.with.deformable.attention.and.query.optimization","name":"transformer-based detection with deformable attention and query optimization","description":"Implements transformer-based detectors (DETR, Deformable DETR, DINO) that replace hand-crafted components (anchors, NMS) with learned query embeddings and attention mechanisms. Deformable DETR adds spatial deformability to attention, focusing on relevant image regions rather than all positions, reducing computational cost from O(n²) to O(n). DINO adds contrastive learning and mixed query selection to improve convergence. These detectors learn to attend to object regions without explicit anchor definitions, enabling end-to-end differentiable detection.","intents":["I want to build a detector that learns what to attend to without hand-crafted anchors or NMS","I need a detector that can handle variable-aspect objects without anchor tuning","I want to leverage transformer pre-training (BERT, ViT) for detection by using transformer backbones"],"best_for":["research teams exploring transformer-based vision models","applications with diverse object aspect ratios where anchor tuning is impractical","practitioners with access to large pre-trained transformer models (ViT, Swin)"],"limitations":["Transformer detectors require significantly more training iterations (>100 epochs) to converge compared to CNN-based detectors (~12 epochs); training time is 5-10x longer","Deformable attention adds complexity; naive implementation has high memory overhead; requires careful optimization for production deployment","Query initialization and matching strategy are critical; poor initialization leads to slow convergence or local minima","Inference is slower than single-stage CNN detectors due to transformer computation; real-time deployment requires model distillation or quantization"],"requires":["PyTorch 1.9+ with CUDA support","mmcv library with transformer utilities","Transformer backbone (Swin, ViT, ResNet with transformer layers)","Significantly more GPU memory (24GB+ recommended) and training time (days on 8 GPUs)"],"input_types":["Images (typically 800x1333 or 1024x1024)","Ground truth bounding boxes with class labels","Optional: pre-trained transformer weights"],"output_types":["Detected bounding boxes with confidence scores","Class predictions per detection","Attention maps showing which image regions the model attends to","Query embeddings (for analysis/visualization)"],"categories":["image-visual","detection-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_5","uri":"capability://image.visual.panoptic.segmentation.with.stuff.and.thing.fusion","name":"panoptic segmentation with stuff and thing fusion","description":"Extends instance segmentation (thing classes: objects with instances) with semantic segmentation (stuff classes: amorphous regions like sky, grass) to produce panoptic segmentation where every pixel has a semantic label and instance ID. The architecture combines an instance segmentation head (Mask R-CNN-style) for things with a semantic segmentation head for stuff, then fuses predictions using a learned fusion module that resolves overlaps and assigns instance IDs. The modular design allows swapping instance/semantic heads independently.","intents":["I need to segment both countable objects (cars, people) and amorphous regions (sky, road) in a single unified output","I want to evaluate detection quality on panoptic metrics (PQ, SQ, RQ) which combine instance and semantic accuracy","I need to build a scene understanding system that understands both object instances and scene context"],"best_for":["autonomous driving perception systems requiring full scene understanding","robotics applications needing both object detection and scene context","research on unified vision tasks combining instance and semantic segmentation"],"limitations":["Panoptic segmentation requires annotations for both instance masks (things) and semantic labels (stuff); dataset preparation is complex and time-consuming","Fusion of instance and semantic predictions is non-trivial; overlapping predictions require careful handling to avoid artifacts","Computational cost is high; requires both instance and semantic heads, increasing memory and latency by ~30-50% vs instance segmentation alone","Evaluation is complex; panoptic quality (PQ) metric is less intuitive than mAP and requires careful implementation"],"requires":["PyTorch 1.9+","mmcv library with panoptic utilities","Dataset with both instance masks (things) and semantic labels (stuff)","Panoptic segmentation evaluation code (COCO panoptic API or custom implementation)"],"input_types":["Images (typically 800x1333 or 1024x1024)","Instance masks for thing classes","Semantic segmentation labels for stuff classes"],"output_types":["Panoptic segmentation map where each pixel has (semantic_label, instance_id)","Per-class panoptic quality (PQ), segmentation quality (SQ), recognition quality (RQ)","Instance masks for things and semantic masks for stuff"],"categories":["image-visual","segmentation-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_6","uri":"capability://image.visual.rotated.object.detection.with.oriented.bounding.boxes","name":"rotated object detection with oriented bounding boxes","description":"Extends standard axis-aligned bounding box detection to rotated bounding boxes (RBBs) defined by center (x, y), size (w, h), and angle θ. This is critical for detecting oriented objects (ships, aircraft, buildings in aerial imagery) where axis-aligned boxes waste space or cause ambiguity. The architecture uses standard detectors (RetinaNet, Faster R-CNN) with modified heads that predict angle in addition to box coordinates, and uses angle-aware NMS that considers rotation when computing IoU. Loss functions account for angle periodicity (0° = 360°).","intents":["I need to detect oriented objects in aerial/satellite imagery where axis-aligned boxes are inefficient","I want to detect ships, aircraft, or buildings with precise orientation information","I need to handle objects at arbitrary angles without rotating the input image"],"best_for":["remote sensing and aerial imagery analysis","object detection in rotated/tilted images","applications where object orientation is semantically important"],"limitations":["Angle prediction is ambiguous due to periodicity (0° = 360°); requires careful loss function design to avoid discontinuities","Rotated NMS is computationally expensive; requires computing rotated IoU which is O(n²) with higher constant factors than axis-aligned IoU","Angle regression is harder to learn than coordinate regression; requires more training data and careful initialization","Evaluation metrics (AP, mAP) are less standardized for rotated detection; different implementations compute rotated IoU differently"],"requires":["PyTorch 1.9+","mmcv library with rotated NMS and rotated IoU computation","Dataset with rotated bounding box annotations (DOTA, HRSC2016, or custom format)","Angle-aware loss function (smooth L1 loss with angle periodicity handling)"],"input_types":["Images (typically large aerial images, 512x512 to 4096x4096)","Rotated bounding boxes defined as (x, y, w, h, angle)","Class labels per box"],"output_types":["Detected rotated bounding boxes with confidence scores","Angle predictions (in degrees or radians)","Class predictions per detection","Evaluation metrics (AP, mAP for rotated detection)"],"categories":["image-visual","detection-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_7","uri":"capability://data.processing.analysis.data.augmentation.pipeline.with.geometric.and.photometric.transforms","name":"data augmentation pipeline with geometric and photometric transforms","description":"Implements a composable data augmentation system where transforms (rotation, flip, crop, color jitter, mosaic) are defined as modular components and applied sequentially during training. Augmentations are specified in config files and applied on-the-fly during data loading, avoiding the need to pre-augment datasets. The system handles coordinate transformation (bounding boxes, masks) automatically when geometric transforms are applied. Advanced augmentations like mosaic (combining 4 images) and mixup are supported for improved robustness.","intents":["I want to apply consistent augmentations (rotation, flip, crop) to images and bounding boxes without manual coordinate transformation","I need to experiment with different augmentation strategies (weak vs strong) by modifying config files","I want to use advanced augmentations (mosaic, mixup) to improve detector robustness without dataset preprocessing"],"best_for":["practitioners training detectors on small datasets where augmentation is critical","researchers studying the effect of augmentation on detector performance","teams needing reproducible augmentation pipelines across experiments"],"limitations":["On-the-fly augmentation adds ~10-20% training time overhead compared to pre-augmented datasets","Complex augmentations (mosaic, mixup) require careful implementation to avoid introducing artifacts or incorrect labels","Coordinate transformation for bounding boxes is non-trivial for rotations/perspective transforms; incorrect implementation causes label corruption","Augmentation hyperparameters (rotation angle range, color jitter magnitude) require tuning per dataset; poor choices degrade performance"],"requires":["PyTorch 1.9+","mmcv library with augmentation utilities","Albumentations or torchvision for geometric/photometric transforms","Proper bounding box annotation format (COCO JSON or Pascal VOC XML)"],"input_types":["Images (any resolution)","Bounding boxes with class labels","Optional: instance masks, keypoints"],"output_types":["Augmented images","Transformed bounding boxes with updated coordinates","Transformed masks/keypoints (if provided)"],"categories":["data-processing-analysis","augmentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_8","uri":"capability://data.processing.analysis.dataset.registry.and.format.conversion.with.multi.format.support","name":"dataset registry and format conversion with multi-format support","description":"Provides a unified dataset interface through a registry system where datasets (COCO, Pascal VOC, LVIS, custom formats) are registered and accessed uniformly. The system handles format conversion (e.g., Pascal VOC XML to COCO JSON), annotation parsing, and dataset statistics computation. Custom datasets can be registered by implementing a simple interface (load_data_list, parse_data_info). The modular design allows adding new dataset formats without modifying the core training loop.","intents":["I want to train a detector on a custom dataset by registering it with MMDetection without writing dataset loading code","I need to convert annotations from Pascal VOC format to COCO format for compatibility","I want to combine multiple datasets (COCO + custom data) in a single training run"],"best_for":["practitioners working with custom datasets or multiple dataset formats","teams migrating between dataset formats (VOC → COCO)","researchers comparing detectors across multiple benchmarks"],"limitations":["Dataset registry requires understanding MMDetection's dataset interface; custom dataset implementation requires inheriting from BaseDataset and implementing required methods","Format conversion can be lossy (e.g., converting from formats with additional metadata); manual verification is needed","Large datasets (ImageNet-scale) require careful memory management during loading; naive implementation causes OOM errors","Dataset statistics (class distribution, image sizes) are not automatically computed; requires manual analysis"],"requires":["PyTorch 1.9+","mmcv library with dataset utilities","Properly formatted dataset annotations (COCO JSON, Pascal VOC XML, or custom format)","Understanding of MMDetection's BaseDataset interface"],"input_types":["Dataset annotations in COCO JSON, Pascal VOC XML, LVIS JSON, or custom format","Image files (JPEG, PNG, etc.)","Optional: metadata files (class names, splits)"],"output_types":["Registered dataset objects accessible via registry","Converted annotations in target format","Dataset statistics (class distribution, image sizes, annotation counts)"],"categories":["data-processing-analysis","dataset-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__cap_9","uri":"capability://data.processing.analysis.model.evaluation.with.standard.metrics.and.custom.evaluation.hooks","name":"model evaluation with standard metrics and custom evaluation hooks","description":"Computes detection metrics (mAP, mAP@50, mAP@75, per-class AP) using standard evaluation protocols (COCO, Pascal VOC, LVIS). The evaluation system is modular: metrics are registered and instantiated via config, allowing custom metrics to be added without modifying the evaluation loop. Evaluation hooks are called at specified intervals during training (e.g., every 10 epochs), enabling early stopping or learning rate adjustment based on validation performance. Results are logged and visualized.","intents":["I want to evaluate my detector on COCO/Pascal VOC using standard metrics without implementing evaluation code","I need to compute custom metrics (per-class recall, false positive analysis) in addition to standard mAP","I want to monitor validation performance during training and save the best checkpoint based on mAP"],"best_for":["practitioners evaluating detectors on standard benchmarks (COCO, Pascal VOC, LVIS)","researchers comparing detector performance across architectures","teams implementing custom evaluation metrics for domain-specific tasks"],"limitations":["Standard metrics (mAP) are computed on the full validation set; no support for streaming evaluation on large datasets","Metric computation is CPU-bound; evaluation can take 5-10 minutes for large datasets, slowing down training","Custom metrics require implementing the metric interface; poorly implemented metrics can introduce bugs or slow down evaluation","Evaluation results are sensitive to NMS threshold and confidence threshold; different thresholds produce different metrics"],"requires":["PyTorch 1.9+","mmcv library with evaluation utilities","COCO API (pycocotools) for COCO metric computation","Properly formatted validation annotations in COCO/VOC format"],"input_types":["Model predictions (bounding boxes, confidence scores, class labels)","Ground truth annotations (COCO JSON, Pascal VOC XML, etc.)","Optional: custom metric definitions"],"output_types":["Standard metrics (mAP, mAP@50, mAP@75, per-class AP)","Custom metrics (if defined)","Evaluation logs and visualizations","Best checkpoint based on validation metric"],"categories":["data-processing-analysis","evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmdetection__headline","uri":"capability://model.training.open.source.object.detection.toolbox","name":"open-source object detection toolbox","description":"MMDetection is a comprehensive open-source toolbox for object detection, instance segmentation, and panoptic segmentation, featuring over 300 pre-trained models and a modular design for easy customization and experimentation.","intents":["best object detection toolbox","object detection framework for research","open-source instance segmentation tool","top panoptic segmentation models","modular object detection solutions"],"best_for":["researchers","developers","data scientists"],"limitations":[],"requires":["PyTorch"],"input_types":["images","annotations"],"output_types":["detection results","segmentation masks"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","Python 3.7+","mmcv library (OpenMMLab's core utilities)","Understanding of detector component interfaces (BaseDetector, BaseHead, BaseBackbone)","PyTorch 1.9+ with CUDA support for distributed training","NCCL backend for multi-GPU synchronization","mmcv library with training utilities","Properly formatted dataset annotations (COCO JSON, Pascal VOC XML, or custom format)","Trained model checkpoint (.pth file)","Model config file (.py) specifying architecture"],"failure_modes":["Registry-based instantiation adds ~5-10ms overhead per model initialization due to dynamic class lookup","Requires understanding of MMDetection's config schema and module interfaces; steep learning curve for newcomers","Custom modules must inherit from base classes and implement required methods (_forward_train, _forward_test) or registration fails silently","Config-based approach obscures control flow; debugging training issues requires understanding config parsing and the training loop implementation","Distributed training requires careful synchronization of batch statistics; incorrect config can cause gradient mismatch across processes","No built-in support for dynamic learning rate scheduling based on validation metrics (e.g., ReduceLROnPlateau); requires custom callbacks","Batch inference requires images to be resized to the same size; variable-size images require padding or multiple forward passes","Test-time augmentation (TTA) increases inference latency by 3-5x (5-10 augmented versions per image); useful for accuracy but impractical for real-time applications","ONNX export requires careful handling of custom operations; some MMDetection components (deformable convolution, rotated NMS) may not export cleanly","TensorRT optimization is NVIDIA-specific; 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