facebookresearch/detrArchived
Detr
This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation.
The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image locations.
The project includes capabilities for distributed training across multiple GPUs and compute nodes, as well as tools for computing accuracy metrics such as Average Precision. It also provides utilities for bounding box coordinate conversion and the integration of pre-trained backbones and external datasets.
Features
- Object Detection - Implements a system that identifies and locates objects within images using bounding boxes and classification.
- Transformer-Based Detectors - Implements a transformer-based object detection model that treats detection as a direct set prediction problem.
- Ground Truth Assignment Algorithms - Uses a Hungarian matcher for assigning predicted bounding boxes to ground truth objects.
- Attention Mechanisms - Implements a global attention mechanism allowing every pixel to interact with all others for long-range dependencies.
- Instance Segmentation Engines - Provides a framework to generate pixel-level masks that isolate individual object instances within a scene.
- Panoptic Segmentation - Combines semantic and instance segmentation to assign both a class label and instance ID to every pixel.
- Feature Extraction - Uses a convolutional backbone to extract initial image feature maps for the transformer.
- Transformer-Based Architectures - Treats object detection as a direct set prediction problem using a transformer encoder and decoder architecture.
- Hungarian Matching Losses - Implements a Hungarian matcher for one-to-one loss calculation between predicted boxes and ground truth.
- Multi-Head Attention Mechanisms - Utilizes multi-head attention in the decoder to refine object queries for bounding box and class predictions.
- Object Query Mechanisms - Employs a set of learnable object queries to probe the image for object locations and classes.
- Set Prediction Frameworks - Treats object detection as a direct set prediction problem using a transformer encoder and decoder.
- Detection Accuracy Metrics - Provides tools for computing accuracy metrics such as Average Precision to validate detection quality.
- Distributed Training - Implements a deep learning setup for training large-scale vision models across multiple GPUs and compute nodes.
- Distributed Training - Supports training large-scale models across multiple GPUs and compute nodes to handle massive datasets.
- Distributed Training Managers - Provides capabilities for executing training jobs across multiple compute nodes and GPUs with synchronized resource allocation.
- Vision Model Fine-Tuning - Provides a framework for fine-tuning the model to identify individual object instances and pixel-level boundaries.
- Detection Model Validation - Provides tools for computing standard performance metrics such as Average Precision to evaluate detection accuracy.
- Object Detection - End-to-end object detection using transformer architectures.
- Object Detection and Segmentation - End-to-end object detection using transformer architectures.
- Perception Models - End-to-end object detection using transformer architectures.
Star history
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