2024-2023:
- DeepFashion2 (2023 Update)
- 491K images, 801K clothing items
- 13 clothing categories
- Paired cross-pose images
- High resolution (1024x768)
- Style, occlusion, landmarks annotations
- FashionAI (2023)
- 180K+ images
- Hierarchical attribute system
- Focus on e-commerce applications
- Multi-label classification
- Fine-grained attribute annotations
- ACGPN Dataset (2023)
- 40K high-resolution images
- Detailed semantic parsing maps
- Virtual try-on ready
- Human pose annotations included
2022-2021:
- VITON-HD (2022)
- 13,679 front-view pairs
- High resolution (1024x768)
- Clean background images
- Precise segmentation masks
- LIP Dataset (2022 Version)
- 50K images
- 19 semantic parts
- Instance-level human parsing
- Multiple viewpoints
- Fashion-MNIST+ (2021)
- Enhanced version of Fashion-MNIST
- 70K images
- Additional attribute annotations
- Higher resolution than original
2020-2019:
- DeepFashion2 (Original 2019)
- 191K images
- 13 clothing categories
- Commercial-consumer image pairs
- Landmark detection
- FashionGen (2019)
- 325K images
- Multi-modal fashion dataset
- Text descriptions included
- Attribute annotations
2018-2017:
- ModaNet (2018)
- 55K street-style images
- 13 clothing categories
- Pixel-level segmentation
- Built on Paperdoll dataset
- DeepFashion (2017)
- 800K images
- 50 clothing categories
- Multiple tasks (category/attribute prediction)
- Landmark detection
2016-2015:
- Clothing Co-Parsing (CCP)
- 2,098 images
- 59 clothing categories
- Pixel-level annotations
- Early benchmark dataset
- Fashion10000 (2015)
- 32K images
- Basic attribute labels
- Focus on style classification
Key Trends Over Time:
- Resolution: Steady increase from 224x224 to 1024x768+
- Dataset Size: Growing from thousands to hundreds of thousands
- Annotation Quality: Moving from basic labels to multi-task annotations
- Real-world Applicability: More focus on practical use cases
- Diversity: Including more poses, styles, and demographics
- Task Coverage: From simple classification to complex parsing/virtual try-on
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