fundamentalvision/Deformable-DETR
Deformable DETR
Deformable-DETR is an object detection system for computer vision that uses a transformer-based encoder-decoder architecture. It identifies and locates objects within images by representing potential targets as a set of learnable queries.
The project employs sampling-based attention to restrict attention to a small set of points around a reference, reducing computational complexity and speeding up convergence. It further utilizes multi-scale feature fusion to detect objects of varying sizes within a single frame.
The system includes capabilities for training models across multiple GPU clusters using distributed data parallelism and evaluating detection precision against standard benchmark datasets.
Features
- Deformable Attention - Implements deformable attention by sampling a small set of points around a reference to reduce complexity.
- Object Detection - Identifies and locates specific objects within images using bounding boxes and classification.
- Detection Model Training - Trains deep learning models to identify and locate multiple objects within images using transformer architectures.
- Multi-Scale Feature Pyramids - Uses multi-scale feature pyramids to detect objects of varying sizes within a single image frame.
- Transformer-Based Architectures - Utilizes a transformer-based encoder-decoder architecture to generate object queries for set prediction.
- Object Query Mechanisms - Employs learnable object queries that interact via attention to predict bounding boxes and object classes.
- Data-Parallel Training - Provides data-parallel training to synchronize gradients across multiple GPU nodes for faster convergence.
- Hungarian Matching Losses - Implements bipartite matching losses to assign predicted boxes to ground truth objects without hand-crafted anchors.
- GPU Training Accelerators - Accelerates training speed by parallelizing workloads across multiple GPU clusters.
- Distributed Training - Scales the training of the object detection model across multiple compute nodes.
- Object Detection - Deformable transformers for end-to-end object detection.
- Object Detection and Segmentation - Deformable transformers for end-to-end object detection.
