facebookresearch/maskrcnn-benchmarkArchived
Maskrcnn Benchmark
This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images.
The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection.
The system includes comprehensive data engineering utilities for parsing COCO datasets, managing custom dataset integration, and performing annotation filtering. It covers the full machine learning workflow, including custom model training with GPU acceleration, weight fine-tuning, batch inference execution, and the calculation of accuracy metrics.
Features
- Instance Segmentation Engines - Ships a high-performance instance segmentation engine for identifying and isolating individual objects with pixel-level masks.
- Bounding Box Detection - Implements bounding box detection to identify the exact positions and classes of objects.
- Computer Vision Training Frameworks - Provides a comprehensive framework for building and fine-tuning neural networks for object detection and instance segmentation.
- Object Detection - Identifies and locates objects within images using a region proposal network and bounding box detection.
- Binary Mask Generators - Implements binary mask generators via a symmetric mask-head architecture to produce pixel-level object masks.
- Segmentation Model Training - Enables training of instance segmentation models using custom user-defined datasets.
- Multi-Stage Inference Pipelines - Features a multi-stage inference pipeline that sequentially chains region proposals with mask-generating heads.
- Tensor Computing Libraries - Leverages PyTorch tensor computing libraries for high-performance mathematical operations and GPU acceleration.
- Modular Backbone Architectures - Employs modular backbone architectures that allow interchangeable feature extractors to be plugged into detection heads.
- Region-based Detection - Utilizes region-based detection architectures that identify candidate object regions before final refinement.
- Bounding Box Representations - Manages bounding box representations and geometric transformations such as cropping and scaling.
- Computer Vision Research - Serves as a modular research framework for experimenting with region proposal networks and mask generation.
- Dataset Integration - Provides interfaces for integrating custom image datasets into the training pipeline using standardized structures.
- Vision Dataset Standardizers - Includes utilities to standardize diverse vision dataset annotations into a uniform format for batch processing.
- Deep Learning Inference Engines - Provides a deep learning inference engine for running real-time or batch predictions of masks and keypoints on GPUs.
- High Throughput Inference - Supports high-throughput model deployment by processing multiple images simultaneously across available hardware.
- Keypoint Detection - Provides keypoint detection to locate anatomical landmarks for human pose estimation.
- GPU Training Accelerators - Utilizes GPU acceleration to optimize model weights and increase training speed.
- Mixed Precision Training - Provides mixed-precision training to reduce memory usage and increase throughput during model optimization.
- Model Inference - Includes a two-stage inference pipeline for running predictions on images or live video feeds.
- Model Fine-Tuning - Adapts pre-trained weights to custom datasets to improve performance on specialized visual recognition tasks.
- Model Predictions - Generates object masks and bounding box predictions using pre-trained models.
- Pose Estimation - Implements anatomical keypoint detection to determine and analyze human physical poses and orientations.
- Dataset Annotation Filtering - Includes utilities to filter out images lacking valid annotations or failing keypoint thresholds.
- COCO Dataset Parsers - Parses COCO format annotations to extract bounding boxes, masks, and keypoints.
- Vision Dataset Loading - Implements utilities for importing and preparing standard vision datasets for training and evaluation.
- Model Component Assembly - Implements a system for instantiating neural network submodules using external hierarchical configuration files.
- Computer Vision - Modular reference for instance segmentation and detection.
- Computer Vision Models - Modular implementation of instance segmentation and object detection.
- Object Detection Frameworks - Modular PyTorch implementation for instance segmentation and object detection.
- Segmentation Architectures - Mask R-CNN benchmark in PyTorch.
- Object Detection - Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.
Star history
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