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ShushanSS/
DeepFashionRetrieval

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Full Project with frontend backend

https://github.com/ShushanSS/TopK_Similar_Images_Retrieval

DeepFashion Retrieval — Fine-tuned ResNet50 Embedder

Fine-tuned ResNet50 image embedder for fashion item retrieval, trained with batch-hard triplet loss and a category-aware PK sampler on the DeepFashion In-shop Clothes Retrieval dataset.

File	Description
`embedder_full_train_epoch_6.pt`	Fine-tuned ResNet50 weights (epoch 6)
`faiss_index.bin`	FAISS index built over embedded gallery images (if included)
`metadata.json` / `config.json`	Embedding dim, class list, preprocessing params (if included)

Setup

bash

pip install -r requirements.txt
python download_weights.py

This downloads the files above into ./weights in your project root.

Generating metadata locally

The metadata CSVs are not included in this repo since they're derived from the raw DeepFashion images, which you need to download separately.

Download the DeepFashion (In-shop Clothes Retrieval) high-res images.
Update DATASET_PATH in prepare_metadata.py to point to your local copy, preserving the original folder structure:
<DATASET_PATH>/<gender>/<clothing_category>/<item_id>/<image>.jpg
Run:
python prepare_metadata.py
This produces original_metadata.csv, full_metadata.csv, and original_metadata_filtered.csv in your working directory.

Model details

Backbone: ResNet50, fine-tuned with batch-hard triplet loss
Sampler: CategoryAwarePKSampler
Retrieval baseline (CLIP zero-shot): Hit Rate@5 = 0.546
Fine-tuned ResNet50: Hit Rate@5 = 0.819 on controlled test set

Usage

python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="ShushanSS/DeepFashionRetrieval",
    local_dir="./weights",
    local_dir_use_symlinks=False,
)

License

MIT (update if different)

ShushanSS/DeepFashionRetrieval