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README.md

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+---
+license: mit
+tags:
+- fashion
+- recommendation
+- pytorch
+- image-to-image
+datasets:
+- shop-the-look
+language:
+- en
+---
+
+# complete the look - fashion compatibility model
+
+scene-based complementary product recommendation model. given an outfit photo, find products that match the style.
+
+## model description
+
+trained on the [shop the look dataset](https://github.com/kang205/STL-Dataset) with fashion SigLIP embeddings.
+
+### architecture
+- **input**: fashion SigLIP embeddings (768-dim)
+- **scene encoder**: MLP with residual blocks → 256-dim
+- **product encoder**: MLP with category embeddings → 256-dim
+- **training**: contrastive + triplet loss with hard negative mining
+
+### performance
+
+| metric | score |
+|--------|-------|
+| R@1    | 12.3% |
+| R@5    | 28.6% |
+| R@10   | 36.6% |
+| MRR    | 0.204 |
+
+## files
+
+- `best_model_v2.pt` - model weights (37MB)
+- `product_embeddings.pkl` - pre-computed product embeddings (110MB, 36K products)
+- `scene_embeddings.pkl` - pre-computed scene embeddings (87MB, 29K scenes)
+
+## usage
+
+```python
+import torch
+import pickle
+
+checkpoint = torch.load("best_model_v2.pt", map_location="cpu", weights_only=False)
+# see github repo for full code
+
+with open("product_embeddings.pkl", "rb") as f:
+    product_embeddings = pickle.load(f)
+```
+
+## code
+
+full training code and web demo: [github repo](https://github.com/USER/complete-the-look)
+
+## citation
+
+based on:
+```bibtex
+@inproceedings{kang2019complete,
+  title={Complete the Look: Scene-based Complementary Product Recommendation},
+  author={Kang, Wang-Cheng and Kim, Eric and Leskovec, Jure and Rosenberg, Charles and McAuley, Julian},
+  booktitle={CVPR},
+  year={2019}
+}
+```

best_model_v2.pt

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product_embeddings.pkl

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scene_embeddings.pkl

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