open-mmlab/mmdetection

Features

• Computer Vision Toolkits - Provides a modular library for developing, training, and evaluating deep learning models for object detection, segmentation, and tracking tasks.
• Object Detection - Develops and benchmarks computer vision models for identifying and localizing objects within images.
• Training Pipelines - Enables executing training scripts using custom configuration files to initiate the model learning process.
• Video Object Tracking - Implementing and evaluating algorithms that detect and maintain object identities across video frames for complex motion analysis tasks.
• Distributed Training Runtimes - Executes training across various hardware environments including single GPUs, multi-GPU clusters, and multi-node setups.
• Model Architectures - Supports defining new model components by registering custom classes to replace default architecture modules.
• Object Tracking Frameworks - Executes multi-object tracking and video instance segmentation on video files.
• Semi-supervised Learning Pipelines - Training detection models by combining labeled and unlabeled data through multi-branch pipelines and teacher-student weight synchronization strategies.
• Training Engines - Manages training loops, distributed execution, custom hooks, and weight optimization for complex neural networks.
• Configuration Management - Provides a hierarchical inheritance system to compose model and training parameters with minimal duplication.
• Data Pipelines - Builds complex data processing workflows to load, augment, and format diverse image and annotation datasets.
• Detection Model Configurations - Provides a modular dictionary structure to define detection algorithms and training hyperparameters.
• Learning Rate Schedulers - Supports specifying parameter schedulers in the configuration file to implement custom learning rate strategies.
• Model Evaluation Frameworks - Enables large-scale model evaluation across single or multi-GPU environments.
• Model Evaluation Tools - The project supports evaluating object detection models using standard metrics or custom datasets to measure precision, recall, and mean average precision across various object categories.
• Model Optimization Frameworks - Converting trained neural network models into optimized formats for efficient inference and production deployment across various hardware backends.
• Training Loop Control - Allows controlling the training process flow by switching between epoch-based and iteration-based loops.
• Transfer Learning - Enables adjusting class numbers for new datasets by modifying the model head while reusing pre-trained weights.
• Data Processing Pipelines - Handles loading, augmentation, annotation conversion, and formatting for diverse computer vision datasets.
• Configuration Management Systems - Provides a hierarchical configuration system for modular, layered parameter overrides.
• Configuration Inheritance - Allows building model structures and training schedules by inheriting existing files to avoid writing configurations from scratch.
• Dataset Abstraction Layers - Provides a standardized abstraction layer for accessing diverse annotation formats and dataset structures.
• Hyperparameter Optimization - Supports optimizing the finetuning process by adjusting hyperparameters such as learning rates and epoch counts.
• Inference Engines - The project enables performing inference on high-resolution images by slicing them into smaller patches, processing them, and merging results with configurable overlap and NMS parameters.
• Model Architecture Wrappers - Defines model architectures by wrapping detectors with multi-branch preprocessors and teacher-student interaction parameters.
• Model Checkpoints - Supports initializing models before finetuning by specifying a URL or local path to pre-trained weights.
• Model Composition Architectures - Enables flexible model composition by routing data through distinct processing branches.
• Model Deployment Utilities - Converts trained research models into optimized formats for production inference and cross-platform deployment.
• Optimization Wrappers - Enables defining optimization wrappers to select optimizers, set learning rates, and apply gradient clipping.
• Training Hooks - Enables registering custom logic to execute at specific lifecycle points during training, validation, or testing.
• Computer Vision - Comprehensive toolbox for object detection.
• Object Detection Frameworks - Open-source PyTorch-based toolbox for object detection research.
• Object Detection - Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.
• Configuration Inheritance - Enables building new models efficiently by inheriting settings from base files and overriding specific fields.
• Dataset Integration - Enables integrating custom or supported datasets into the training pipeline by updating configuration parameters.
• Experiment Tracking - Stores training metrics and visualization data in local or external backends.
• Inference Tools - Provides command-line scripts to execute object detection on images, webcam feeds, and video files.
• Model Interfaces - The project provides a unified high-level interface for object detection inference that supports pre-trained models, custom configurations, and automatic weight management for various input types.
• Test Time Augmentation - Applies flipping and scaling during inference to improve prediction accuracy.
• Video Tracking Data Management - Provides specialized structures to manage video-based tracking datasets, supporting clip-based training and key frame sampling.
• Data Transformation Pipelines - Allows composing sequences of data transformation operations to process and format image data.
• Command Line Configuration - Allows updating nested configuration keys during script execution using command-line arguments.
• Configuration Management - Defines model architectures, training hyperparameters, and data pipelines through composable and inheritable configuration files.
• Model Conversion - Converts trained models into backend-specific formats like ONNX for deployment and inference.
• Component Registries - Uses a central registry to map configuration strings to classes for modular component swapping.
• Benchmarks - Benchmarks model training and inference performance by measuring throughput, memory usage, and accuracy.
• Data Loading Utilities - Manages the ratio of labeled and unlabeled data samples within each training batch using multi-source samplers.
• Data Preparation Utilities - Provides utilities to organize data files and convert annotations into standard formats for training pipelines.
• Dataset Management - Provides a unified interface for managing detection data samples, ground truth, and metadata during training and inference.
• Evaluation Metrics - Computes spatial overlap accuracy between bounding boxes to evaluate detection performance.
• Loss Management Systems - Enables calculating and returning a dictionary of losses and metrics from model forward passes.
• Model Component Registries - Allows defining and registering custom model components like backbones, necks, heads, or loss functions.
• Model Serving - Supports model deployment and serving using dedicated inference servers.
• Performance Metrics - The project calculates average precision metrics for detection models by evaluating precision-recall curves or specific recall points across single or multiple scales.
• Training Loop Schedulers - Allows switching training loops from epoch-based to iteration-based by updating schedulers and data samplers.
• Weight Initialization - Allows defining initializer types and override rules within model configurations to set initial weights.
• Custom Data Augmentation Frameworks - Enables defining and registering custom data augmentation steps within training pipelines.
• Dataset Configuration Systems - Enables configuring custom datasets by defining paths and transformation pipelines.
• Dataset Preparation Tools - The project provides scripts to prepare public detection datasets by downloading, extracting, and symlinking them into the project directory structure for configuration compatibility.
• Package Managers - Provides dedicated tools to install necessary dependencies and the project package using source-based methods.
• Lifecycle Hook Systems - Implements a pluggable event system for executing custom logic at specific training or inference stages.
• Model Benchmarks - The project enables evaluating object detection and instance segmentation model robustness by testing performance against various image corruptions and severity levels using analysis scripts.

Star history