Fashion ai dataset.

 ModaNet (2018) was groundbreaking but there have been several more recent datasets and models for fashion segmentation and analysis. Here are some notable recent ones:

DeepFashion2 (2023 Update)

• 491K images with 801K clothing items
• 13 clothes categories (similar to ModaNet)
• More detailed annotations including style, occlusion, zoom-in
• Higher quality annotations and more diverse images
• Link: https://github.com/switchablenorms/DeepFashion2

VITON-HD (2022)

• High resolution virtual try-on dataset
• 13,679 front-view woman/clothing image pairs
• High quality segmentation masks
• Particularly good for virtual try-on applications

FashionAI Dataset (2023)

• From Alibaba
• Over 180K images
• Focus on attribute recognition
• Detailed hierarchical attribute annotations
• More modern fashion styles and better image quality

LIP (Look Into Person) Dataset (2022 version)

• 50,000 images with pixel-level annotations
• 19 semantic human part labels
• Multiple viewpoints and poses
• Human parsing focused but includes detailed clothing segmentation

ACGPN Dataset (2023)

• 40,000 high-resolution person images
• Detailed parsing maps
• Semantic segmentation for clothes
• Focuses on both parsing and virtual try-on

Key Improvements in Recent Datasets:

1. Higher resolution images
2. Better annotation quality
3. More diverse poses and viewpoints
4. More modern fashion styles
5. Better handling of occlusion and layering
6. More detailed attribute annotations
7. Multi-task annotations (segmentation + attributes + landmarks)

For your specific use case, I would recommend:

1. DeepFashion2 as your primary dataset - it's the most comprehensive and recent
2. Augment with ACGPN if you need higher resolution images
3. Consider FashionAI if you need very detailed attribute recognition