outfit_inspo

Overview & Model Architecture

This model repository hosts a multi-modal vision-language feature extractor based on OpenAI's CLIP architecture, optimized for digital wardrobe management and intelligent outfit recommendation pipelines.

By mapping garment photographs and aesthetic mood queries into a shared 512-dimensional latent vector space, the system computes visual compatibility scores and cosine distances between clothing items (Top, Bottom, Shoes, and Accessories).

Key Features

• 512-Dimensional Visual Style Embeddings: Extracts compact vector representations for garment images to build personal digital closets.
• Multi-Modal Matching: Matches visual anchor pieces (e.g., a specific jacket) with text-based mood or event contexts (e.g., "Casual Urban Chic", "Night Out").
• Polyvore & Fashion Compatibility: Trained/evaluated using compatibility structures inspired by the Polyvore Outfits and DeepFashion benchmarks.
• Continuous Active Learning: Designed to ingest real-time user interaction logs (like / dislike feedback pairs) to dynamically adapt recommendation weights to personal style preferences.

license: mit datasets:

• owj0421/polyvore-outfits
• mvasil/polyvore-outfits
• sahirp/deepfashion2
• lirus18/deepfashion language:
• en
• tr metrics:
• accuracy
• precision
• recall
• f1
• mean_iou base_model:
• openai/clip-vit-base-patch32 pipeline_tag: zero-shot-classification library_name: transformers tags:
• vision
• clip
• fashion
• style-embeedings
• outfit-recommendation

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