fashion-attribute-lab

Fashion Attribute Lab

Fashion Attribute Lab is an experimental multi-task computer-vision checkpoint for fashion product attribute extraction.

The model predicts seven attributes from a product image:

• Gender
• Master category
• Subcategory
• Article type
• Base colour
• Season
• Usage

Model Status

This repository contains an experimental checkpoint, not the primary production model.

The experiment investigates whether spatial colour features, mild class balancing, test-time augmentation, and validation-tuned consistency thresholds can improve an already strong fashion attribute classifier.

Architecture

The model uses:

• CLIP ViT-B/32 image encoder
• Seven task-specific classification heads
• Global HSV and RGB colour statistics
• Spatial colour-grid features
• Hierarchical residual connections
• Article-type-to-gender residual connection
• Validation-selected test-time augmentation
• Lightweight consistency correction

Dataset

ashraq/fashion-product-images-small

The held-out test set contains 6,611 images.

Corrected Test Metrics

Exact-match accuracy requires all seven attributes to be correct for the same image.

Per-Attribute Results

Raw Model Metrics

The following metrics were calculated before consistency correction.

Raw and corrected metrics are reported separately for transparency.

Inference Configuration

• Test-time augmentation enabled: True
• Consistency thresholds selected using validation data only
• Final test data was not used for threshold selection
• Single-image average latency: 12.92 ms
• Single-image P95 latency: 13.74 ms
• Latency measured on an NVIDIA Tesla P100 GPU

Production Decision

This experimental checkpoint achieved measurable but relatively small gains over the existing production checkpoint.

The simpler production model was retained because it provides nearly equivalent predictive performance with:

• Lower inference latency
• A simpler inference pipeline
• Lower deployment complexity
• Easier maintenance
• Better alignment with the existing application

This repository is preserved as an optimization experiment and reproducible research checkpoint.

Repository Files

model.pt
config.json
label_maps.json
consistency_rules.json
consistency_thresholds.json
metrics.json
README.md

Loading

This repository contains a custom PyTorch architecture and cannot be loaded directly with AutoModelForImageClassification.from_pretrained().

The project implementation must:

1. Load config.json
2. Load label_maps.json
3. Initialize AutoCatalogFinalClassifier
4. Load model.pt
5. Generate global and spatial colour features
6. Apply TTA when enabled
7. Optionally apply the saved consistency thresholds

Intended Use

This checkpoint is intended for:

• Fashion attribute-classification experiments
• Multi-task learning analysis
• Colour-feature research
• Test-time augmentation evaluation
• Performance-versus-latency comparisons
• Reproducible portfolio experimentation

Limitations

• Rare colour and usage classes remain difficult
• Base-colour labels may be visually ambiguous
• TTA increases single-image inference latency
• Performance may decrease on marketplace images outside the training distribution
• Season and usage are not always directly visible from an image
• The model is not intended to infer personal attributes about people

The gender label represents the dataset's product-target category, not the gender of a person.

Version

Name: AutoCatalogAI
Version: spatial-tta-experiment
Role: Experimental checkpoint
Architecture: AutoCatalogFinalClassifier
Base encoder: openai/clip-vit-base-patch32
TTA: True
Test samples: 6,611