Conditional DETR ResNet-50
| Property | Value |
|---|---|
| Parameter Count | 43.5M |
| License | Apache 2.0 |
| Framework | PyTorch |
| Training Data | COCO 2017 |
| Paper | Link |
What is conditional-detr-resnet-50?
Conditional DETR ResNet-50 is an advanced object detection model that introduces a conditional cross-attention mechanism to address the slow training convergence issues found in traditional DETR models. Developed by Microsoft, this model achieves remarkable training efficiency, converging 6.7× faster than its predecessors while maintaining high detection accuracy.
Implementation Details
The model employs a conditional spatial query approach within its decoder embedding for multi-head cross-attention. This architecture enables each cross-attention head to focus on specific spatial regions, significantly improving the efficiency of object detection and localization.
- ResNet-50 backbone architecture
- Transformer-based encoder-decoder structure
- Conditional cross-attention mechanism
- F32 tensor type implementation
- Trained on COCO 2017 dataset (118k annotated images)
Core Capabilities
- Fast training convergence (6.7× faster than traditional DETR)
- Efficient object detection and localization
- Robust performance across various object scales
- Simplified training process through conditional spatial queries
- Support for PyTorch inference
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctiveness lies in its conditional cross-attention mechanism that learns spatial queries from decoder embeddings, allowing each attention head to focus on specific regions of interest. This approach significantly reduces training time while maintaining detection accuracy.
Q: What are the recommended use cases?
The model is ideal for object detection tasks in real-world scenarios, particularly when working with the COCO dataset categories. It's especially suitable for applications requiring fast training iteration cycles while maintaining high detection accuracy.
