fashion ai dataset, ordered by years

 2024-2023:

1. DeepFashion2 (2023 Update)

• 491K images, 801K clothing items
• 13 clothing categories
• Paired cross-pose images
• High resolution (1024x768)
• Style, occlusion, landmarks annotations

2. FashionAI (2023)

• 180K+ images
• Hierarchical attribute system
• Focus on e-commerce applications
• Multi-label classification
• Fine-grained attribute annotations

3. ACGPN Dataset (2023)

• 40K high-resolution images
• Detailed semantic parsing maps
• Virtual try-on ready
• Human pose annotations included

2022-2021:

1. VITON-HD (2022)

• 13,679 front-view pairs
• High resolution (1024x768)
• Clean background images
• Precise segmentation masks

2. LIP Dataset (2022 Version)

• 50K images
• 19 semantic parts
• Instance-level human parsing
• Multiple viewpoints

3. Fashion-MNIST+ (2021)

• Enhanced version of Fashion-MNIST
• 70K images
• Additional attribute annotations
• Higher resolution than original

2020-2019:

1. DeepFashion2 (Original 2019)

• 191K images
• 13 clothing categories
• Commercial-consumer image pairs
• Landmark detection

2. FashionGen (2019)

• 325K images
• Multi-modal fashion dataset
• Text descriptions included
• Attribute annotations

2018-2017:

1. ModaNet (2018)

• 55K street-style images
• 13 clothing categories
• Pixel-level segmentation
• Built on Paperdoll dataset

2. DeepFashion (2017)

• 800K images
• 50 clothing categories
• Multiple tasks (category/attribute prediction)
• Landmark detection

2016-2015:

1. Clothing Co-Parsing (CCP)

• 2,098 images
• 59 clothing categories
• Pixel-level annotations
• Early benchmark dataset

2. Fashion10000 (2015)

• 32K images
• Basic attribute labels
• Focus on style classification

Key Trends Over Time:

1. Resolution: Steady increase from 224x224 to 1024x768+
2. Dataset Size: Growing from thousands to hundreds of thousands
3. Annotation Quality: Moving from basic labels to multi-task annotations
4. Real-world Applicability: More focus on practical use cases
5. Diversity: Including more poses, styles, and demographics
6. Task Coverage: From simple classification to complex parsing/virtual try-on

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

"fashion attribute recognition" or "clothing parsing"

 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:

1. 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

2. 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:

1. DeepFashion Dataset

• Over 800,000 images
• 50 clothing categories
• Multiple attributes per item
• Includes landmarks and segmentation
• Good for both segmentation and attribute recognition

2. ModaNet

• About 55,000 fully annotated images
• 13 clothing categories
• Instance segmentation masks
• Strong street-style focus

3. Fashion-MNIST

• Simpler dataset, good for initial testing
• 70,000 grayscale images
• 10 clothing categories
• Limited attributes

4. Clothing Co-Parsing (CCP) Dataset

• 2,098 fashion images
• 59 clothing categories
• Pixel-level annotations
• Good for fine-grained parsing

Recommended Approach:

1. 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

2. Training Strategy:

• Pre-train on large datasets like DeepFashion
• Fine-tune on your specific use case
• Use multi-task learning for different attributes

3. Implementation Frameworks:

• PyTorch or TensorFlow
• Consider using MMFashion (open-source fashion analysis toolbox)
• HuggingFace Transformers for recent vision models