Update README.md

README.md

@@ -1,6 +1,20 @@
 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- fashion
+- articleType
+- product
+- siglip2
 ---
+# **Fashion-Product-articleType**
+
+> **Fashion-Product-articleType** is a vision model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images into one of 141 article types.
 
 ```py
 Classification Report:
@@ -151,4 +165,215 @@ Face Scrub and Exfoliator     0.0000    0.0000    0.0000         4
                  accuracy                         0.8911     44072
                 macro avg     0.7131    0.6174    0.6361     44072
              weighted avg     0.8877    0.8911    0.8846     44072
-```
+```
+
+The model predicts one of the following **article types** for fashion products, such as:
+
+- **0:** Accessory Gift Set  
+- **1:** Baby Dolls  
+- **2:** Backpacks  
+- **3:** Bangle  
+- **...**  
+- **140:** Wristbands  
+
+---
+
+# **Run with Transformers 🤗**
+
+```bash
+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-articleType"  # Replace with your actual model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Accessory Gift Set",
+    1: "Baby Dolls",
+    2: "Backpacks",
+    3: "Bangle",
+    4: "Basketballs",
+    5: "Bath Robe",
+    6: "Beauty Accessory",
+    7: "Belts",
+    8: "Blazers",
+    9: "Body Lotion",
+    10: "Body Wash and Scrub",
+    11: "Booties",
+    12: "Boxers",
+    13: "Bra",
+    14: "Bracelet",
+    15: "Briefs",
+    16: "Camisoles",
+    17: "Capris",
+    18: "Caps",
+    19: "Casual Shoes",
+    20: "Churidar",
+    21: "Clothing Set",
+    22: "Clutches",
+    23: "Compact",
+    24: "Concealer",
+    25: "Cufflinks",
+    26: "Cushion Covers",
+    27: "Deodorant",
+    28: "Dresses",
+    29: "Duffel Bag",
+    30: "Dupatta",
+    31: "Earrings",
+    32: "Eye Cream",
+    33: "Eyeshadow",
+    34: "Face Moisturisers",
+    35: "Face Scrub and Exfoliator",
+    36: "Face Serum and Gel",
+    37: "Face Wash and Cleanser",
+    38: "Flats",
+    39: "Flip Flops",
+    40: "Footballs",
+    41: "Formal Shoes",
+    42: "Foundation and Primer",
+    43: "Fragrance Gift Set",
+    44: "Free Gifts",
+    45: "Gloves",
+    46: "Hair Accessory",
+    47: "Hair Colour",
+    48: "Handbags",
+    49: "Hat",
+    50: "Headband",
+    51: "Heels",
+    52: "Highlighter and Blush",
+    53: "Innerwear Vests",
+    54: "Ipad",
+    55: "Jackets",
+    56: "Jeans",
+    57: "Jeggings",
+    58: "Jewellery Set",
+    59: "Jumpsuit",
+    60: "Kajal and Eyeliner",
+    61: "Key chain",
+    62: "Kurta Sets",
+    63: "Kurtas",
+    64: "Kurtis",
+    65: "Laptop Bag",
+    66: "Leggings",
+    67: "Lehenga Choli",
+    68: "Lip Care",
+    69: "Lip Gloss",
+    70: "Lip Liner",
+    71: "Lip Plumper",
+    72: "Lipstick",
+    73: "Lounge Pants",
+    74: "Lounge Shorts",
+    75: "Lounge Tshirts",
+    76: "Makeup Remover",
+    77: "Mascara",
+    78: "Mask and Peel",
+    79: "Mens Grooming Kit",
+    80: "Messenger Bag",
+    81: "Mobile Pouch",
+    82: "Mufflers",
+    83: "Nail Essentials",
+    84: "Nail Polish",
+    85: "Necklace and Chains",
+    86: "Nehru Jackets",
+    87: "Night suits",
+    88: "Nightdress",
+    89: "Patiala",
+    90: "Pendant",
+    91: "Perfume and Body Mist",
+    92: "Rain Jacket",
+    93: "Ring",
+    94: "Robe",
+    95: "Rompers",
+    96: "Rucksacks",
+    97: "Salwar",
+    98: "Salwar and Dupatta",
+    99: "Sandals",
+    100: "Sarees",
+    101: "Scarves",
+    102: "Shapewear",
+    103: "Shirts",
+    104: "Shoe Accessories",
+    105: "Shoe Laces",
+    106: "Shorts",
+    107: "Shrug",
+    108: "Skirts",
+    109: "Socks",
+    110: "Sports Sandals",
+    111: "Sports Shoes",
+    112: "Stockings",
+    113: "Stoles",
+    114: "Sunglasses",
+    115: "Sunscreen",
+    116: "Suspenders",
+    117: "Sweaters",
+    118: "Sweatshirts",
+    119: "Swimwear",
+    120: "Tablet Sleeve",
+    121: "Ties",
+    122: "Ties and Cufflinks",
+    123: "Tights",
+    124: "Toner",
+    125: "Tops",
+    126: "Track Pants",
+    127: "Tracksuits",
+    128: "Travel Accessory",
+    129: "Trolley Bag",
+    130: "Trousers",
+    131: "Trunk",
+    132: "Tshirts",
+    133: "Tunics",
+    134: "Umbrellas",
+    135: "Waist Pouch",
+    136: "Waistcoat",
+    137: "Wallets",
+    138: "Watches",
+    139: "Water Bottle",
+    140: "Wristbands"
+}
+
+def classify_article_type(image):
+    """Predicts the article type for 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_article_type,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Article Type Prediction Scores"),
+    title="Fashion-Product-articleType",
+    description="Upload a fashion product image to predict its article type (e.g., T-shirt, Jeans, Handbag, etc)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model is best suited for:
+
+- **Fashion E-commerce Tagging & Categorization**  
+- **Automated Product Labeling for Catalogs**  
+- **Enhanced Product Search & Filtering**  
+- **Retail Analytics and Product Type Breakdown**