AI model for clothing analysis and attribute extraction from person images. This is often called "fashion attribute recognition" or "clothing parsing" in computer vision.
For this task, you'll want to consider several components:
- Person/Clothing Segmentation
- First, you'll need to segment different clothing items
- Models like DeepFashion2 or ModaNet provide good architectures for this
- You can use Mask R-CNN or similar instance segmentation models as a base
- Attribute Recognition For each segmented clothing item, you'll need to recognize:
- Category (top, pants, hat, etc.)
- Color
- Material
- Pattern
- Style/type
- Specific attributes (collar type, sleeve length, etc.)
Available Datasets:
- DeepFashion Dataset
- Over 800,000 images
- 50 clothing categories
- Multiple attributes per item
- Includes landmarks and segmentation
- Good for both segmentation and attribute recognition
- ModaNet
- About 55,000 fully annotated images
- 13 clothing categories
- Instance segmentation masks
- Strong street-style focus
- Fashion-MNIST
- Simpler dataset, good for initial testing
- 70,000 grayscale images
- 10 clothing categories
- Limited attributes
- Clothing Co-Parsing (CCP) Dataset
- 2,098 fashion images
- 59 clothing categories
- Pixel-level annotations
- Good for fine-grained parsing
Recommended Approach:
- Model Architecture:
- Use a two-stage approach: a. First stage: Mask R-CNN or YOLOv8 for segmentation b. Second stage: ResNet or EfficientNet backbone with attribute-specific heads
- Training Strategy:
- Pre-train on large datasets like DeepFashion
- Fine-tune on your specific use case
- Use multi-task learning for different attributes
- Implementation Frameworks:
- PyTorch or TensorFlow
- Consider using MMFashion (open-source fashion analysis toolbox)
- HuggingFace Transformers for recent vision models
