
![13.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ME_3kB4xkOUBuKpnHj5CX.png)

# **Fashion-Product-masterCategory**

> **Fashion-Product-masterCategory** is a vision model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images into high-level master categories.
> 
```py
Classification Report:
                precision    recall  f1-score   support

   Accessories     0.9611    0.9698    0.9654     11244
       Apparel     0.9855    0.9919    0.9887     21361
      Footwear     0.9952    0.9936    0.9944      9197
    Free Items     0.0000    0.0000    0.0000       105
          Home     0.0000    0.0000    0.0000         1
 Personal Care     0.9638    0.9219    0.9424      2139
Sporting Goods     1.0000    0.0400    0.0769        25

      accuracy                         0.9803     44072
     macro avg     0.7008    0.5596    0.5668     44072
  weighted avg     0.9779    0.9803    0.9788     44072
```

The model predicts one of the following master categories:

- **0:** Accessories  
- **1:** Apparel  
- **2:** Footwear  
- **3:** Free Items  
- **4:** Home  
- **5:** Personal Care  
- **6:** Sporting Goods

---

# **Run with Transformers 🤗**

```python
!pip install -q transformers torch pillow gradio
```

```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Fashion-Product-masterCategory"  # Replace with your actual model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    0: "Accessories",
    1: "Apparel",
    2: "Footwear",
    3: "Free Items",
    4: "Home",
    5: "Personal Care",
    6: "Sporting Goods"
}

def classify_master_category(image):
    """Predicts the master category of a fashion product."""
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))}
    return predictions

# Gradio interface
iface = gr.Interface(
    fn=classify_master_category,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Master Category Prediction Scores"),
    title="Fashion-Product-masterCategory",
    description="Upload a fashion product image to predict its master category (Accessories, Apparel, Footwear, etc.)."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()
```

---

# **Intended Use**

This model can be applied to:

- **E-commerce product categorization**  
- **Automated tagging of product catalogs**  
- **Enhancing search and filtering options**  
- **Data annotation pipelines for fashion datasets**
accessories apparel footwear free items home personal care sporting goods