facebookresearch/detectron2
Detectron2
Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration.
The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimation framework for mapping anatomical landmarks and dense 2D surfaces of the human body.
The platform covers a broad range of capabilities, including visual recognition training with pre-trained model libraries, dataset integration and annotation preparation, and model performance benchmarking. It also supports visual inference deployment through containerization and mobile platform optimization.
Features
- Computer Vision Training Frameworks - Provides a PyTorch-based framework for building and fine-tuning models for object detection, segmentation, and pose estimation.
- Object Detection - Provides a primary framework for identifying and locating multiple objects in images using bounding boxes.
- Bounding Box Detection - Provides a specialized library for identifying and locating multiple objects via bounding boxes.
- Instance Segmentation Engines - Ships engines for instance segmentation to generate pixel-level masks for individual objects.
- Image Segmentation - Provides comprehensive image segmentation capabilities to identify and delineate individual objects.
- Panoptic Segmentation - Implements panoptic segmentation to assign both class labels and instance IDs to every pixel.
- Distributed Training - Supports distributed training across multiple GPUs using PyTorch's distributed backend.
- Feature Extraction - Implements backbone-based feature extraction using pre-trained convolutional and transformer networks.
- Computer Vision Inference - Executes pre-trained vision models on images, videos, or webcam feeds for real-time detection and segmentation.
- Vision Model Training - Provides a high-level interface and custom loops for training object detection and segmentation models.
- Model Registries - Provides a centralized model registry that maps configuration strings to specific neural network implementations.
- Vision Model Training - Provides a framework for training and evaluating vision models using custom datasets and multi-GPU acceleration.
- Pose Estimation - Implements a framework for detecting and mapping human body landmarks and 3D surfaces from images.
- Panoptic Segmentation - Implements a workflow to assign a class label and instance ID to every pixel in an image.
- Object Detection and Segmentation - Combines bounding box detection, panoptic segmentation, and rendering to map object boundaries.
- Tensor Computation Graphs - Built on PyTorch to leverage dynamic computational graphs and automatic differentiation for visual tasks.
- 3D - Locates and classifies objects within three-dimensional space using fully convolutional networks.
- Few-Shot Detection - Implements capabilities to identify object categories using only a small number of labeled examples.
- Keypoint Detection - Provides capabilities to locate specific anatomical landmarks on the human body via keypoint detection.
- Transformer-Based Detectors - Implements object detection architectures that utilize transformer self-attention mechanisms.
- Visual Representation Learning Frameworks - Ships frameworks for training image encoders to capture visual patterns using unsupervised contrastive learning.
- Model Exporters - Converts trained PyTorch models into serialized formats for production execution without the training framework.
- Model Evaluation Metrics - Calculates accuracy and performance metrics for trained models using specific checkpoints and configurations.
- Computer Vision and Recognition - Offers a research-oriented environment for training and evaluating complex computer vision and recognition models.
- Mobile - Provides processes to train and deploy vision models optimized for performance on mobile platforms.
- Mobile Training Pipelines - Provides a management system for the end-to-end pipeline of training and deploying vision models on mobile platforms.
- Model Exporting - Provides utilities to export trained weights into portable formats for production and mobile deployment.
- Multi-GPU Training Utilities - Accelerates the training of vision models by distributing workloads across multiple GPUs.
- Dense - Estimates dense human pose by mapping image pixels to a 3D surface of the body.
- Visual Recognition Classifiers - Provides deep learning architectures for analyzing image content and classifying objects based on visual features.
- Dataset Tensor Mappings - Converts raw image annotations into standardized tensor formats for consistent model training.
- Containerized Deployments - Supports packaging the vision environment and dependencies into containers for consistent cross-hardware execution.
- Pipeline Component Modularization - Utilizes a modular architecture to assemble detection pipelines from interchangeable processing components.
- Computer Vision - Research platform for object detection and segmentation.
- Computer Vision and Image Processing - Research platform for object detection and segmentation.
- Computer Vision Frameworks - Platform for object detection, segmentation, and pose estimation.
- Computer Vision Models - Next-generation system for state-of-the-art object detection algorithms.
- Computer Vision Tools - A library for object detection, segmentation, and pose estimation.
- Object Detection - Mask R-CNN implementation for instance segmentation and detection.
- Object Detection Frameworks - Next-generation research platform for object detection and segmentation.
- Segmentation - Listed in the “Segmentation” section of the The Incredible Pytorch awesome list.
