facebookresearch/detectron2

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.

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