metadata
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 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
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
citation
based on:
@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}
}