DETR ResNet-50
| Property | Value |
|---|---|
| Parameter Count | 41.6M |
| License | Apache 2.0 |
| Paper | End-to-End Object Detection with Transformers |
| Training Data | COCO 2017 (118k images) |
| Performance | 42.0 AP on COCO validation |
What is detr-resnet-50?
DETR-ResNet-50 is a groundbreaking object detection model that combines a ResNet-50 backbone with transformer architecture for end-to-end object detection. Developed by Facebook Research, it revolutionizes traditional object detection by eliminating the need for hand-crafted components like anchor boxes and non-maximum suppression.
Implementation Details
The model employs an encoder-decoder transformer architecture with a CNN backbone. It processes images through 100 object queries, each designed to detect specific objects in the image. The model uses a bipartite matching loss during training, utilizing the Hungarian algorithm for optimal query-annotation matching.
- ResNet-50 backbone for feature extraction
- Transformer encoder-decoder architecture
- Linear layer for class prediction
- MLP for bounding box detection
- Bipartite matching loss function
Core Capabilities
- Object detection with 42.0 AP on COCO dataset
- Processing of images with multiple objects
- End-to-end training capability
- Efficient handling of variable object counts
- Direct set prediction without post-processing
Frequently Asked Questions
Q: What makes this model unique?
DETR's uniqueness lies in its end-to-end approach to object detection using transformers, eliminating traditional hand-crafted components while maintaining competitive performance. It's trained on COCO 2017 and can process images targeting 100 potential objects simultaneously.
Q: What are the recommended use cases?
The model is ideal for general object detection tasks, particularly those requiring COCO-trained categories. It's especially suitable for scenarios requiring clean architecture without post-processing steps, and where batch processing of images is needed.
