DETR ResNet-101 DC5
Deepest DETR variant combining ResNet-101 backbone with dilated convolutions
DETR ResNet-101 DC5 combines the deepest ResNet backbone (101 layers) with dilated convolutions in the final stage, representing the most powerful standard DETR configuration. This model offers the highest accuracy among non-deformable DETR variants, particularly excelling at small object detection in complex scenes.
When to Use DETR ResNet-101 DC5
Use this model when you need:
- Maximum DETR accuracy without using Deformable variants
- Excellent small object detection
- Deep feature representations for complex scenes
- Large datasets (5,000+ images) to leverage full capacity
Strengths
- Highest accuracy among standard DETR variants
- Best small object detection in standard DETR family
- Deep features + high resolution = excellent representations
- Strong performance on challenging detection scenarios
Weaknesses
- Most memory-intensive DETR variant (16GB+ GPU recommended)
- Slowest training and inference in DETR family
- Requires large datasets to avoid overfitting
- Overkill for simple detection tasks
Parameters
Training Configuration
Training Images: Folder with images Annotations: COCO-format JSON Batch Size (Default: 2) - Range: 1-2 (very memory-intensive) Epochs (Default: 1) - Range: 1-6 Learning Rate (Default: 5e-5) Eval Steps (Default: 1)
Configuration Tips
- Requires 16-24GB GPU for comfortable training
- batch_size typically limited to 2 even with powerful GPUs
- Best for large, complex datasets (5,000+ images)
- Consider Deformable DETR as alternative for better efficiency
Expected Performance
- Highest accuracy in standard DETR family
- 4-6% better than DETR ResNet-50 on small objects
- Best when maximum CNN-based DETR accuracy needed
Comparison with Alternatives
vs DETR ResNet-101: Choose DC5 for small objects, standard for memory efficiency
vs Deformable DETR: Deformable usually better choice - faster, more efficient, comparable accuracy