DETR ResNet-101
End-to-end object detection with deeper ResNet-101 backbone for higher accuracy
DETR with ResNet-101 backbone is the deeper variant of the standard DETR model, offering improved accuracy through a more powerful feature extractor. The 101-layer ResNet backbone captures richer visual representations, making this model ideal when maximum CNN-based accuracy is required for transformer detection systems.
When to Use DETR ResNet-101
Use DETR ResNet-101 when you need higher accuracy than DETR ResNet-50 and have:
- Large datasets (5,000+ annotated images)
- Complex detection scenarios requiring deep features
- Sufficient GPU resources (12GB+ VRAM)
- Acceptance of slower training times for better results
Strengths
- Higher accuracy than DETR ResNet-50 (2-3% mAP improvement)
- Deeper feature hierarchies for complex patterns
- Strong for challenging detection scenarios
- Same elegant transformer architecture as standard DETR
- Better feature representations for fine-grained detection
Weaknesses
- 2x slower training than DETR ResNet-50
- Higher memory requirements (12-16GB GPU needed)
- Still struggles with small objects (use DC5 or Deformable variants)
- Diminishing returns on small datasets
- Overfitting risk with limited data
Parameters
Training Configuration
Training Images: Folder containing object images Annotations: COCO-format JSON file with bounding boxes and labels Batch Size (Default: 2) - Range: 1-4, use 2-4 with 12-16GB GPU Epochs (Default: 1) - Range: 1-8, typically 3-5 for fine-tuning Learning Rate (Default: 5e-5) - Use 1e-4 for large datasets (>10k images) Eval Steps (Default: 1)
Configuration Tips
Dataset Recommendations
- Minimum: 2,000+ annotated images
- Optimal: 5,000+ images for noticeable improvement over ResNet-50
- Large: 10,000+ images for maximum benefit
Training Settings
- batch_size=2-4 depending on GPU memory
- epochs=3-5 for fine-tuning
- learning_rate=5e-5 standard, 1e-4 for large datasets
- Monitor both losses and mAP metrics
Expected Performance
- Small datasets (2k images): Consider ResNet-50 instead (may overfit)
- Medium datasets (5k images): 2-3% better mAP than ResNet-50
- Large datasets (10k+ images): 3-5% mAP improvement, strong performance
Comparison with Alternatives
vs DETR ResNet-50: Choose 101 for maximum accuracy with large datasets, choose 50 for faster training or smaller datasets
vs Deformable DETR: Deformable converges faster and handles small objects better; choose 101 only if you prefer standard DETR architecture