| Property | Value |
|---|---|
| Parameter Count | 47.2M |
| License | MIT |
| Tensor Type | F32 |
| Paper | SegFormer Paper |
What is segformer_b3_clothes?
Segformer_b3_clothes is a specialized image segmentation model based on the SegFormer architecture, fine-tuned specifically for clothing and human parsing tasks. Trained on the ATR dataset, it can accurately segment 18 different clothing and body part categories with an impressive mean accuracy of 0.80 and mean IoU of 0.69.
Implementation Details
The model utilizes transformer-based architecture optimized for semantic segmentation tasks. It processes images through the SegformerImageProcessor and outputs detailed segmentation maps for various clothing items and body parts.
- Built on SegFormer B3 architecture
- Supports 18 distinct segmentation categories
- Implements efficient bilinear interpolation for output processing
- Achieves high accuracy in common clothing categories (0.87 for upper clothes, 0.90 for pants)
Core Capabilities
- Precise segmentation of clothing items including upper-clothes, pants, dresses, and accessories
- Accurate body part detection including face (0.92 accuracy), arms, and legs
- Background separation with 0.99 accuracy
- Real-time processing capabilities for various image sizes
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful SegFormer architecture with specialized training for clothing segmentation, achieving high accuracy across a wide range of clothing items and body parts. Its balanced performance across different categories makes it particularly suitable for fashion and retail applications.
Q: What are the recommended use cases?
The model is ideal for e-commerce platforms, virtual try-on applications, fashion analysis, and human parsing tasks. It performs exceptionally well in segmenting common clothing items and can be integrated into systems requiring detailed human parsing capabilities.