DETR Segmentation ResNet-50 DC5
Panoptic segmentation with dilated convolutions for better small object masks
DETR Segmentation ResNet-50 DC5 combines panoptic segmentation capabilities with dilated convolutions in the backbone's final stage. The dilated convolutions increase spatial resolution of features, significantly improving segmentation quality for small objects while maintaining DETR's elegant transformer-based architecture.
When to Use
Use this model when you need:
- Panoptic segmentation with many small objects
- Better boundary precision than standard DETR segmentation
- Transformer-based approach with improved spatial resolution
- Datasets with objects at multiple scales
Strengths
- Better small object segmentation than standard variants
- Higher resolution features through dilation
- Improved boundary delineation
- Same transformer elegance as DETR
Weaknesses
- Very high memory usage (dilated features + segmentation)
- Slower than standard ResNet-50 variant
- Requires 16GB+ GPU minimum
- Still challenging for very tiny objects (<16x16 pixels)
Parameters
Training Configuration
Training Images: Image folder Segmentation Masks: Mask folder Batch Size (Default: 2) - Usually limited to 1-2 Epochs (Default: 1) - Range: 1-6 Learning Rate (Default: 1e-4) Eval Steps (Default: 1)
Configuration Tips
- Best for datasets with small objects (cars, people at distance, etc.)
- batch_size=1-2 due to memory requirements
- Requires 16-20GB GPU for training
- 3-5% IoU improvement on small objects vs standard variant
Expected Performance
Small Object IoU: 5-10% better than standard DETR Segmentation Overall mIoU: 2-3% improvement Trade-off: 1.5-2x slower inference
Comparison
vs Standard DETR Segmentation: Choose DC5 when small objects critical, standard for memory efficiency
vs Mask R-CNN: DC5 better for small objects but requires more resources