Add model card metadata for Hugging Face

README.md

@@ -1,28 +1,3 @@
-# StyleFinder – Fashion Visual Search with CLIP
-
-This repository includes two fine-tuned CLIP models for image-based fashion retrieval:
-
-| Model         | Stage        | Rank-1 | mAP   |
-|---------------|--------------|--------|-------|
-| ViT-B/16      | Stage 3 v4   | 46.24% | 0.3481|
-| ResNet-50     | Stage 3 v3   | 53.95% | 0.4265|
-
----
-
-## 🧠 Model Details
-
-- **ViT-B/16 (Transformer-based, 512-dim):** Jointly fine-tuned using SupCon + ArcFace + BNNeck.
-- **RN50 (CNN-based, 1024-dim):** Fine-tuned with prompt-structured Stage 3 configuration.
-- Dataset: [DeepFashion – In-shop Clothes Retrieval](https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html)
-
----
-
-## 📦 How to Use
-
-```python
-from model_loader import load_model
-model = load_model("vitb16")  # or "rn50"
-
 ---
 license: mit
 tags:
@@ -58,3 +33,52 @@ model-index:
             value: 0.3481
             name: mAP (ViT-B/16)
 ---
+
+# 👗 StyleFinder – AI-Powered Fashion Visual Search
+
+**StyleFinder** is a deep learning-based image retrieval system fine-tuned on the DeepFashion In-shop Clothes dataset using [CLIP](https://openai.com/research/clip). It enables users to upload an image and retrieve visually similar fashion items using both zero-shot and fine-tuned CLIP variants.
+
+---
+
+## 🧠 Supported Models
+
+| Model         | Stage        | Description                                  |
+|---------------|--------------|----------------------------------------------|
+| ViT-B/16      | Stage 3 v4   | Best fine-tuned transformer-based model      |
+| RN50          | Stage 3 v3   | Best fine-tuned CNN-based model              |
+| ViT-B/16      | Zero-shot    | Official OpenAI pretrained CLIP              |
+| RN50          | Zero-shot    | Official OpenAI pretrained CLIP              |
+
+---
+
+## 📊 Evaluation Results
+
+| Metric     | ViT-B/16 (v4) | RN50 (v3) |
+|------------|---------------|-----------|
+| Rank-1     | 46.24%        | **53.95%** |
+| mAP        | 0.3481        | **0.4265** |
+
+---
+
+## 🖼️ Precomputed Gallery Features
+
+Gallery embeddings are stored as `.pt` files for fast cosine similarity search.
+
+| File Name                              | Description                       |
+|----------------------------------------|-----------------------------------|
+| `vitb16_stage3_v4_gallery.pt`          | Fine-tuned ViT-B/16 gallery       |
+| `rn50_stage3_v3_gallery.pt`            | Fine-tuned RN50 gallery           |
+| `vitb16_zeroshot_gallery.pt`           | Official CLIP ViT-B/16 gallery    |
+| `rn50_zeroshot_gallery.pt`             | Official CLIP RN50 gallery        |
+
+These are stored in the `gallery_features/` directory and can be loaded with `load_gallery_features()`.
+
+---
+
+## ⚙️ How to Use
+
+### 🔹 Load a Model
+
+```python
+from model_loader import load_model
+model, preprocess = load_model(arch="vitb16", stage="stage3")  # or rn50 / zeroshot