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README.md

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+---
+license: mit
+tags:
+  - fashion
+  - image-retrieval
+  - resnet50
+  - triplet-loss
+  - deepfashion
+---
+
+# 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)