facebookresearch/DetectronArchived
Detectron
Detectron is a PyTorch object detection framework and computer vision research platform. It provides implementations of neural network architectures for locating and identifying objects in images, including Mask R-CNN for generating instance segmentation masks and RetinaNet for one-stage detection.
The platform supports computer vision prototyping and object detection research through the deployment of pre-trained baseline models. This allows for the rapid implementation and evaluation of visual recognition systems.
Its capabilities cover image object localization and instance segmentation workflows. These are supported by structural components such as feature pyramid networks, region-based convolutional networks, and two-stage detection pipelines.
Features
- Object Detection - Identifies and locates multiple objects within images using bounding boxes and neural network classification.
- Instance Segmentation Engines - Generates pixel-level masks to isolate individual object instances using architectures like Mask R-CNN.
- Object Mask Generators - Implements Mask R-CNN to create precise outlines for objects using bounding boxes and segmentation masks.
- Feature Extraction - Implements deep convolutional neural networks to extract high-level semantic representations from images.
- Computer Vision Platforms - Offers a comprehensive environment for developing and experimenting with image recognition and object detection algorithms.
- Region-based Detection - Provides a two-stage pipeline using region proposal networks followed by refined classification and mask generation.
- Anchor Box Generators - Provides utilities for generating reference bounding boxes of various scales and ratios for object detection.
- Computer Vision Evaluation Tools - Supports the evaluation and prototyping of visual recognition systems using pre-trained baseline models.
- Region Alignment - Uses bilinear interpolation to preserve spatial information and avoid misalignment in region proposals.
- Multi-Scale Feature Pyramids - Utilizes architectures that aggregate hierarchical image representations to detect objects of varying sizes.
- Object Detection Models - Provides a platform for experimenting with neural network architectures designed for object localization.
- One-Stage Detectors - Implements RetinaNet as a one-stage detector utilizing focal loss to handle foreground-background class imbalance.
- Visión artificial - Research platform for state-of-the-art object detection algorithms.
- Face Detection Utilities - Object detection framework utilizing focal loss for dense detection.
- Object Detection - Dense object detection using focal loss mechanisms.
- Object Detection Frameworks - FAIR's research platform for implementing advanced object detection algorithms.
- Region Based Detectors - High-performance research platform for object detection and segmentation.
- Segmentation Loss Functions - Provides focal loss implementations for dense object detection tasks.
- Video Analysis - Group normalization techniques for visual recognition tasks.
