"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

 

Installing cuDNN on Ubuntu 22.04

Step 1: Download cuDNN

1. Go to https://developer.nvidia.com/cudnn
2. Sign in to your NVIDIA Developer account (or create one if needed)
3. Navigate to Downloads
4. Find and download cuDNN v9.6.0 for Ubuntu 22.04 (.deb package)

Step 2: Install cuDNN

Run these commands in order:

# Install the downloaded package
sudo dpkg -i cudnn-local-repo-ubuntu2204-9.6.0_1.0-1_amd64.deb

# Copy the keyring
sudo cp /var/cudnn-local-repo-ubuntu2204-9.6.0/cudnn-*-keyring.gpg /usr/share/keyrings/

# Update package list
sudo apt-get update

# Install cuDNN
sudo apt-get -y install cudnn

# Install CUDA 12 specific package
sudo apt-get -y install cudnn-cuda-12

Step 3: Verify Installation

# Check if cuDNN is installed correctly
find /usr -name "libcudnn.so*"

Note: Direct download links won't work - you must download through NVIDIA's website after logging in.

Pedestrian and human attribute dataset.

 

For Pedestrian Detection:

1. CityPersons - High-quality pedestrian detection dataset with diverse urban scenes from multiple European cities
2. Caltech Pedestrian Dataset - Contains approximately 250,000 frames with 350,000 bounding boxes and 2,300 unique pedestrians
3. INRIA Person Dataset - Includes full-body pedestrians in various poses and backgrounds
4. MOT (Multiple Object Tracking) Dataset - Contains pedestrians in crowded scenes

For Human Attribute Analysis:

1. RAP (Richly Annotated Pedestrian) Dataset - Over 40 attributes including clothing types, colors, and accessories
2. PETA Dataset - Large-scale surveillance person attribute dataset with 19,000 images
3. Market-1501 Attribute Dataset - Contains 27 attributes for clothing and personal items
4. DeepFashion Dataset - Focuses on clothing items with detailed annotations

Some considerations when choosing a dataset:

• Make sure to check the license terms for each dataset
• Consider the image quality and diversity needed for your specific use case
• Check if the annotations match your requirements (bounding boxes, attributes, etc.)
• Verify that the dataset size is sufficient for your model training needs