Deformable DETR
| Property | Value |
|---|---|
| Parameter Count | 40.2M |
| License | Apache 2.0 |
| Framework | PyTorch |
| Paper | View Paper |
| Dataset | COCO 2017 |
What is deformable-detr?
Deformable DETR is an advanced object detection model that combines the power of transformers with deformable attention mechanisms. Developed by SenseTime, it builds upon the original DETR architecture by introducing deformable attention, which enables more efficient processing of feature maps and better handling of objects at different scales.
Implementation Details
The model utilizes a ResNet-50 backbone coupled with an encoder-decoder transformer architecture. It processes images through 100 object queries and employs a bipartite matching loss during training. The model operates in F32 precision and has been optimized for the COCO dataset with 118k annotated images.
- Encoder-decoder transformer architecture with ResNet-50 backbone
- Bipartite matching loss with Hungarian algorithm optimization
- 100 object queries for detection
- Linear layer for class labels and MLP for bounding boxes
Core Capabilities
- High-accuracy object detection in complex scenes
- Efficient handling of multi-scale objects
- End-to-end training capability
- Support for PyTorch inference
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its deformable attention mechanism, which allows for adaptive sampling of input features, making it more efficient than traditional transformer-based detectors. It maintains high accuracy while reducing computational complexity.
Q: What are the recommended use cases?
This model is ideal for complex object detection tasks, particularly in scenarios requiring accurate detection of objects at various scales. It's well-suited for applications in surveillance, autonomous driving, and general computer vision tasks that require robust object detection.
