SkalskiP/fashion-assistant

Our idea is to combine the power of computer vision model and LLMs. We use YOLO, CLIP and DINOv2 to extract high-level features from images. We pass the prompt, along with the extracted features, to LLM, allowing for advanced image dataset queries.

37
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Emerging

The pipeline chains YOLO for garment detection, CLIP for semantic clothing attributes, and DINOv2 for fine-grained visual features—concatenating these embeddings as context for LLM inference to enable natural language queries over fashion image datasets. The project targets outfit composition and style-matching workflows, with an open-source annotated dataset available via Roboflow for training and evaluation.

118 stars. No commits in the last 6 months.

Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 11 / 25

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Stars

118

Forks

9

Language

Jupyter Notebook

License

MIT

Last pushed

May 30, 2023

Commits (30d)

0

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