SegFormer-B0

SegFormer-B0 is the smallest and most efficient variant of the SegFormer family, combining a hierarchical transformer encoder with an All-MLP decoder for semantic segmentation. Despite its compact size, it achieves strong performance while being significantly faster and more memory-efficient than traditional semantic segmentation models, making it ideal for practical deployments.

When to Use SegFormer-B0

SegFormer-B0 is ideal for:

• Semantic segmentation tasks (pixel-level classification without instances)
• Efficient deployment requiring smaller models
• Real-time or near-real-time segmentation applications
• Datasets with 1,000+ images
• When transformer benefits desired with CNN-like efficiency

Strengths

• Efficient architecture: Small size with strong performance
• Hierarchical features: Multi-scale representations like CNNs
• Simple decoder: All-MLP head avoids heavy decoder complexity
• Good speed-accuracy trade-off: Fast inference for a transformer
• Flexible resolution: Handles various input sizes
• Lower memory: More efficient than FCN or DeepLab models

Weaknesses

• Lower absolute accuracy than larger SegFormer variants (B1-B5)
• Not designed for instance segmentation (semantic only)
• Requires more data than some CNN approaches
• Still slower than very lightweight models like Mobile-ViT

Parameters

Training Configuration

Training Images: Folder with images Segmentation Masks: Folder with semantic masks (pixel values = class IDs)

Num Classes (Default: 150)

• Number of semantic classes in your dataset
• ADE20K uses 150, Cityscapes uses 19, adjust for your data
• Background is typically class 0

Batch Size (Default: 8)

• Range: 4-32
• More efficient than heavy segmentation models
• Use 8-16 with 12GB GPU, 16-32 with 16GB+

Epochs (Default: 50)

• Range: 20-100
• Semantic segmentation needs more epochs than classification
• 50 epochs typical for fine-tuning

Learning Rate (Default: 6e-5)

• Very small learning rate (0.00006)
• Critical to use this low rate for stability
• Do not increase above 1e-4

Configuration Tips

Dataset Requirements

• Minimum: 1,000 images with semantic masks
• Optimal: 3,000+ images for robust performance
• Masks should be PNG with pixel values = class IDs

Training Settings

• batch_size=8-16 depending on image resolution and GPU
• epochs=50 standard, reduce to 30 if overfitting
• learning_rate=6e-5 (very important - don't increase much)
• num_classes must match your dataset exactly

Class Handling

• Class 0 typically background/unlabeled
• Ensure masks have values 0 to (num_classes-1)
• Handle class imbalance with weighted loss if possible

Expected Performance

mIoU (mean Intersection over Union):

• Simple datasets: 0.65-0.75
• Complex datasets (ADE20K-style): 0.40-0.50
• Better than lightweight CNNs, close to heavy models

Training Time: 30-60 minutes per epoch on 5k images (RTX 4090) Inference Speed: 15-30ms per image (512x512 resolution)

Example Use Cases

Autonomous Driving Scene Parsing

Scenario: Segment road, sidewalk, vehicles, pedestrians, etc. in driving scenes

Configuration:

Model: SegFormer-B0
Num Classes: 19 (Cityscapes classes)
Batch Size: 16
Epochs: 50
Learning Rate: 6e-5
Images: 3,000 driving scenes

Why SegFormer-B0: Multi-scale features for various object sizes, efficient for real-time needs, good accuracy

Medical Image Segmentation

Scenario: Segment organs or lesions in CT/MRI scans

Configuration:

Model: SegFormer-B0
Num Classes: 5 (background + 4 organ types)
Batch Size: 8
Epochs: 80
Learning Rate: 6e-5
Images: 2,000 medical scans

Why SegFormer-B0: Efficient transformer for medical data, good detail capture, reasonable training time

Satellite Image Segmentation

Scenario: Land cover classification from aerial/satellite imagery

Configuration:

Model: SegFormer-B0
Num Classes: 10 (water, forest, urban, etc.)
Batch Size: 12
Epochs: 60
Learning Rate: 6e-5
Images: 4,000 satellite images

Why SegFormer-B0: Multi-scale features for varying land cover sizes, efficient processing, handles high-res images

Common Issues and Solutions

Poor Boundary Segmentation

Problem: Class boundaries are fuzzy or inaccurate

Solutions:

1. Increase input resolution
2. Train for more epochs (try 80-100)
3. Check mask annotation quality at boundaries
4. Ensure learning rate not too high

Class Imbalance Issues

Problem: Model predicts majority class excessively

Solutions:

1. Use weighted loss (emphasize rare classes)
2. Ensure balanced representation in training
3. Check if background class dominating (very common)
4. May need to collect more minority class examples

Underfitting

Problem: mIoU remains low even with training

Solutions:

1. Train much longer (100+ epochs may be needed)
2. Verify learning_rate is 6e-5 (critical)
3. Check data preprocessing and normalization
4. Ensure num_classes matches dataset
5. Consider larger SegFormer variant (B1 or B2)

Out of Memory

Problem: CUDA out of memory

Solutions:

1. Reduce batch_size (try 4 or 2)
2. Reduce input image resolution (512x512 or 384x384)
3. Enable gradient checkpointing
4. Close other GPU applications

Comparison with Alternatives

SegFormer-B0 vs Larger SegFormer Variants

Choose SegFormer-B0 when:

• Want efficient, lightweight model
• Inference speed important
• Limited GPU resources (8-12GB)
• Good accuracy sufficient

Choose B1/B2/B3 when:

• Maximum accuracy needed
• Have powerful GPU (16GB+)
• Can afford slower inference
• Complex fine-grained segmentation

SegFormer-B0 vs Mask R-CNN

Choose SegFormer-B0 when:

• Need semantic segmentation (no instances)
• Dense pixel classification
• Efficient transformer desired
• Don't need to separate object instances

Choose Mask R-CNN when:

• Need instance segmentation
• Must separate individual objects
• Want detection + segmentation
• Proven production reliability critical

SegFormer-B0 vs DETR Segmentation

Choose SegFormer-B0 when:

• Semantic-only segmentation sufficient
• Need faster training and inference
• Want efficient model
• Don't need panoptic segmentation

Choose DETR Segmentation when:

• Need panoptic (semantic + instance)
• Want unified detection and segmentation
• Can afford more compute
• Transformer reasoning across image important

SegFormer-B0 vs Traditional FCN/DeepLab

Choose SegFormer-B0 when:

• Want modern transformer approach
• Better feature representations desired
• Have sufficient training data (1k+ images)
• GPU available for training

Choose FCN/DeepLab when:

• Very limited data (<500 images)
• Proven traditional approach preferred
• CPU inference required
• Simplest possible architecture desired