Deformable DETR with Box Refinement and Two-Stage Detection
| Property | Value |
|---|---|
| Parameters | 41.3M |
| License | Apache 2.0 |
| Framework | PyTorch |
| Dataset | COCO 2017 |
| Paper | Deformable DETR Paper |
What is deformable-detr-with-box-refine-two-stage?
This is an advanced implementation of the Deformable DETR architecture developed by SenseTime, designed for efficient object detection. It combines transformer-based architecture with deformable attention mechanisms and includes both box refinement and two-stage detection capabilities. The model uses a ResNet-50 backbone and is specifically optimized for the COCO object detection task.
Implementation Details
The model employs an encoder-decoder transformer architecture with a convolutional backbone. It utilizes 100 object queries to detect objects in images, with two specialized heads: a linear layer for class labels and an MLP for bounding box prediction. The model is trained using a bipartite matching loss and Hungarian algorithm for optimal query-annotation mapping.
- Deformable attention mechanism for efficient processing
- Two-stage detection pipeline for improved accuracy
- Box refinement capabilities for precise object localization
- ResNet-50 backbone architecture
- Trained on COCO 2017 dataset (118k images)
Core Capabilities
- End-to-end object detection
- Multi-object tracking in complex scenes
- Efficient handling of objects at different scales
- High-precision bounding box prediction
- Support for 80 COCO object classes
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its combination of deformable attention mechanisms, two-stage detection, and box refinement capabilities. These features enable more efficient and accurate object detection compared to standard DETR models, particularly for objects at different scales.
Q: What are the recommended use cases?
The model is ideal for complex object detection tasks, particularly in scenarios requiring precise object localization. It's well-suited for applications in autonomous driving, surveillance systems, retail analytics, and general computer vision tasks requiring robust object detection.
