segformer_b2_clothes

Maintained By
mattmdjaga

SegFormer B2 Clothes Segmentation Model

PropertyValue
Parameter Count27.4M
LicenseMIT
FrameworkPyTorch/ONNX
PaperSegFormer Paper
Mean Accuracy80%
Mean IoU69%

What is segformer_b2_clothes?

segformer_b2_clothes is a specialized semantic segmentation model based on the SegFormer architecture, fine-tuned specifically for clothes and human parsing. It's built on the efficient transformer-based SegFormer B2 backbone and trained on the ATR dataset to identify 18 different clothing and body part categories with high precision.

Implementation Details

The model leverages transformer architecture for efficient semantic segmentation, implementing a 27.4M parameter network that processes images through the SegFormer pipeline. It supports both PyTorch and ONNX frameworks, making it versatile for different deployment scenarios.

  • Supports 18 distinct categories including clothing items, body parts, and accessories
  • Achieves impressive accuracy metrics with 80% mean accuracy and 69% mean IoU
  • Implements efficient image processing through SegformerImageProcessor
  • Features F32 tensor type for precise predictions

Core Capabilities

  • High-accuracy segmentation of clothing items (87% for upper clothes, 90% for pants)
  • Precise human body part detection (92% accuracy for face detection)
  • Accessory recognition including bags (91% accuracy) and belts
  • Real-time image processing capabilities

Frequently Asked Questions

Q: What makes this model unique?

This model uniquely combines the efficient SegFormer architecture with specialized training for clothing segmentation, achieving high accuracy across 18 different categories while maintaining reasonable computational requirements with just 27.4M parameters.

Q: What are the recommended use cases?

The model is ideal for e-commerce applications, virtual try-on systems, fashion analytics, and human parsing tasks. It's particularly effective for applications requiring detailed clothing item segmentation and body part detection.

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