DETR ResNet-101
| Property | Value |
|---|---|
| Parameters | 60.7M |
| License | Apache 2.0 |
| Framework | PyTorch |
| Paper | End-to-End Object Detection with Transformers |
| Performance | 43.5 AP on COCO |
What is detr-resnet-101?
DETR-ResNet-101 is a groundbreaking object detection model that combines a ResNet-101 backbone with transformer architecture for end-to-end object detection. Developed by Facebook, it represents a departure from traditional object detection approaches by eliminating the need for hand-crafted components like non-maximum suppression or anchor generation.
Implementation Details
The model utilizes an encoder-decoder transformer architecture with a convolutional backbone. It processes images through 100 object queries, each designed to detect specific objects in the image. The model employs two specialized heads: a linear layer for class prediction and an MLP for bounding box detection.
- ResNet-101 backbone for feature extraction
- Transformer-based encoder-decoder architecture
- Bipartite matching loss function
- Hungarian algorithm for optimal query-annotation matching
- Trained on COCO 2017 dataset (118k images)
Core Capabilities
- High-accuracy object detection (43.5 AP on COCO)
- End-to-end detection without post-processing
- Support for multiple object detection in single images
- Efficient parallel processing of queries
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its end-to-end approach to object detection using transformers, eliminating traditional hand-crafted components while achieving competitive performance. It's trained using a novel bipartite matching loss and processes images holistically rather than using region proposals.
Q: What are the recommended use cases?
The model is ideal for complex object detection tasks, particularly those involving multiple objects in varying scales and contexts. It's well-suited for applications in autonomous driving, surveillance, retail analytics, and general computer vision tasks requiring accurate object detection.
