DETR Segmentation ResNet-101

DETR Segmentation ResNet-101 extends the DETR object detection architecture to panoptic segmentation, combining semantic and instance segmentation in a unified transformer framework. With a deep ResNet-101 backbone, it delivers high-quality pixel-level segmentation masks alongside object detection, making it ideal for comprehensive scene understanding.

When to Use

Use DETR Segmentation ResNet-101 when you need:

• Panoptic segmentation (both semantic background and instance objects)
• High accuracy segmentation with transformer benefits
• Unified model for detection and segmentation
• Large datasets (3,000+ annotated images)

Strengths

• Unified architecture for detection and segmentation
• Panoptic capabilities - handles both stuff and things
• Deep backbone for complex patterns
• Transformer reasoning across entire image
• No hand-crafted post-processing

Weaknesses

• Very memory-intensive (16GB+ GPU required)
• Slow training and inference
• Requires substantial training data
• Small object segmentation challenging

Parameters

Training Configuration

Training Images: Folder with images Segmentation Masks: Folder with corresponding mask images Batch Size (Default: 2) - Range: 1-4, typically limited to 2 Epochs (Default: 1) - Range: 1-8 Learning Rate (Default: 1e-4) - Higher than detection due to mask head Eval Steps (Default: 1)

Configuration Tips

• Minimum 3,000+ images with quality masks
• batch_size=2 typical even with 24GB GPU
• learning_rate=1e-4 (higher than detection)
• epochs=3-5 for fine-tuning
• Monitor IoU and Dice score

Expected Performance

Semantic IoU: 0.6-0.75 depending on task complexity Instance mAP: 35-45% on COCO-style datasets Training Time: 5-8 hours per epoch on 5k images (RTX 4090)

Comparison

vs DETR Segmentation ResNet-50: Choose 101 for maximum accuracy with large datasets (5k+ images)

vs Mask R-CNN: Choose DETR for unified approach and transformer benefits; choose Mask R-CNN for faster training and proven production use