DETR-ResNet50: Object Detection
DETR-ResNet50 is an efficient object detection model that combines DETR (DEtection TRansformers) with the ResNet-50 backbone network. DETR is an end-to-end object detection framework based on Transformer architecture, utilizing self-attention mechanisms to detect objects in images without relying on traditional region proposals. ResNet-50 serves as the backbone for feature extraction, leveraging residual connections to effectively learn multi-level features of the image, enhancing detection capability. DERT-ResNet50 strikes a balance between high detection accuracy and reduced complexity, making it suitable for object detection tasks in complex scenes, with applications in autonomous driving, video surveillance, and real-time object detection.
Source model
- Input shape: 480x480
- Number of parameters: 39.60M
- Model size: 158.03M
- Output shape: 1x100x92, 1x100x4
Source model repository: DETR-ResNet50
Performance Reference
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Inference & Model Conversion
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License
Source Model: APACHE-2.0
Deployable Model: APLUX-MODEL-FARM-LICENSE