DETR Segmentation ResNet-50
Standard DETR panoptic segmentation for balanced performance
DETR Segmentation ResNet-50 is the standard variant of DETR's panoptic segmentation architecture, offering balanced performance between accuracy and computational requirements. It extends DETR object detection with pixel-level segmentation masks, enabling unified scene understanding through transformers.
When to Use
Use DETR Segmentation ResNet-50 for:
- General panoptic segmentation tasks
- Learning transformer-based segmentation
- Medium to large datasets (2,000+ images)
- When you need both detection and segmentation
Strengths
- Balanced accuracy and efficiency
- Elegant end-to-end architecture
- No NMS or anchor-related post-processing
- Good starting point for segmentation projects
Weaknesses
- Struggles with very small objects
- Memory-intensive (12GB+ GPU needed)
- Slower convergence than specialized models
- Lower accuracy than ResNet-101 variant
Parameters
Training Configuration
Training Images: Folder with images Segmentation Masks: Folder with masks Batch Size (Default: 2) - Range: 2-4 Epochs (Default: 1) - Range: 1-8 Learning Rate (Default: 1e-4) Eval Steps (Default: 1)
Configuration Tips
- Works well with 2,000+ annotated images
- batch_size=2-4 with 16GB GPU
- learning_rate=1e-4 for segmentation (higher than detection)
- epochs=5-8 for fine-tuning
Expected Performance
mIoU: 0.55-0.70 depending on dataset Instance mAP: 30-40% COCO-style Good balance of speed and accuracy
Comparison
vs ResNet-101 variant: Choose ResNet-50 for faster training, 101 for maximum accuracy
vs Mask R-CNN: DETR simpler architecture but slower; Mask R-CNN faster and more mature