Update README.md

README.md

@@ -11,6 +11,9 @@ tags:
 - fashion
 - product
 ---
+# **Fashion-Product-subCategory**
+
+> **Fashion-Product-subCategory** is a vision model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images into 45 fine-grained subcategories for retail and e-commerce applications.
 
 ```py
 Classification Report:
@@ -65,4 +68,125 @@ Loungewear and Nightwear     0.7604    0.6703    0.7125       464
                 accuracy                         0.9568     44072
                macro avg     0.7091    0.6270    0.6412     44072
             weighted avg     0.9535    0.9568    0.9540     44072
-```
+```
+
+The model predicts one of the following product subcategories:
+
+```json
+"id2label": {
+  "0": "Accessories",
+  "1": "Apparel Set",
+  "2": "Bags",
+  "3": "Bath and Body",
+  "4": "Beauty Accessories",
+  "5": "Belts",
+  "6": "Bottomwear",
+  "7": "Cufflinks",
+  "8": "Dress",
+  "9": "Eyes",
+  "10": "Eyewear",
+  "11": "Flip Flops",
+  "12": "Fragrance",
+  "13": "Free Gifts",
+  "14": "Gloves",
+  "15": "Hair",
+  "16": "Headwear",
+  "17": "Home Furnishing",
+  "18": "Innerwear",
+  "19": "Jewellery",
+  "20": "Lips",
+  "21": "Loungewear and Nightwear",
+  "22": "Makeup",
+  "23": "Mufflers",
+  "24": "Nails",
+  "25": "Perfumes",
+  "26": "Sandal",
+  "27": "Saree",
+  "28": "Scarves",
+  "29": "Shoe Accessories",
+  "30": "Shoes",
+  "31": "Skin",
+  "32": "Skin Care",
+  "33": "Socks",
+  "34": "Sports Accessories",
+  "35": "Sports Equipment",
+  "36": "Stoles",
+  "37": "Ties",
+  "38": "Topwear",
+  "39": "Umbrellas",
+  "40": "Vouchers",
+  "41": "Wallets",
+  "42": "Watches",
+  "43": "Water Bottle",
+  "44": "Wristbands"
+}
+```
+
+---
+
+# **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-subCategory"  # 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 Set", 2: "Bags", 3: "Bath and Body", 4: "Beauty Accessories",
+    5: "Belts", 6: "Bottomwear", 7: "Cufflinks", 8: "Dress", 9: "Eyes", 10: "Eyewear",
+    11: "Flip Flops", 12: "Fragrance", 13: "Free Gifts", 14: "Gloves", 15: "Hair", 16: "Headwear",
+    17: "Home Furnishing", 18: "Innerwear", 19: "Jewellery", 20: "Lips", 21: "Loungewear and Nightwear",
+    22: "Makeup", 23: "Mufflers", 24: "Nails", 25: "Perfumes", 26: "Sandal", 27: "Saree",
+    28: "Scarves", 29: "Shoe Accessories", 30: "Shoes", 31: "Skin", 32: "Skin Care", 33: "Socks",
+    34: "Sports Accessories", 35: "Sports Equipment", 36: "Stoles", 37: "Ties", 38: "Topwear",
+    39: "Umbrellas", 40: "Vouchers", 41: "Wallets", 42: "Watches", 43: "Water Bottle", 44: "Wristbands"
+}
+
+def classify_subcategory(image):
+    """Predicts the subcategory 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_subcategory,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Subcategory Prediction Scores"),
+    title="Fashion-Product-subCategory",
+    description="Upload a fashion product image to predict its subcategory (e.g., Dress, Shoes, Accessories, etc.)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model is best suited for:
+
+- **Product Subcategory Tagging**: Automatically assign fine-grained subcategories to fashion product listings.  
+- **Improved Search & Filters**: Enhance customer experience by enabling better filtering and browsing.  
+- **Catalog Structuring**: Streamline fashion catalog organization at scale for large e-commerce platforms.  
+- **Automated Inventory Insights**: Identify trends in product categories for sales, inventory, and marketing analysis.