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license: mit
tags:
- fashion
- image-retrieval
- resnet50
- triplet-loss
- deepfashion
---
# 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.
## Contents
| 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.
1. Download the DeepFashion (In-shop Clothes Retrieval) high-res images.
2. 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
```
3. Run:
```bash
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)
|