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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.

Classification Report:
 precision recall f1-score support

 Accessories 0.9700 0.7519 0.8472 129
 Apparel Set 0.9011 0.7736 0.8325 106
 Bags 0.9275 0.9767 0.9515 3053
 Bath and Body 1.0000 0.1111 0.2000 9
 Beauty Accessories 0.0000 0.0000 0.0000 3
 Belts 0.9684 0.9840 0.9761 811
 Bottomwear 0.9445 0.9754 0.9597 2685
 Cufflinks 0.8870 0.9444 0.9148 108
 Dress 0.7857 0.7364 0.7603 478
 Eyes 0.7500 0.0882 0.1579 34
 Eyewear 0.9898 0.9991 0.9944 1073
 Flip Flops 0.8558 0.9102 0.8822 913
 Fragrance 0.9280 0.9530 0.9404 1001
 Free Gifts 0.0000 0.0000 0.0000 104
 Gloves 0.7000 0.3500 0.4667 20
 Hair 0.8824 0.7895 0.8333 19
 Headwear 0.9403 0.8601 0.8984 293
 Home Furnishing 0.0000 0.0000 0.0000 1
 Innerwear 0.9763 0.9347 0.9550 1806
 Jewellery 0.9689 0.9527 0.9607 1079
 Lips 0.9292 0.9271 0.9282 425
Loungewear and Nightwear 0.7604 0.6703 0.7125 464
 Makeup 0.7904 0.8745 0.8303 263
 Mufflers 1.0000 0.0526 0.1000 38
 Nails 0.9450 0.9892 0.9666 278
 Perfumes 0.0000 0.0000 0.0000 6
 Sandal 0.8720 0.7940 0.8312 961
 Saree 0.9320 0.9953 0.9626 427
 Scarves 0.6316 0.7119 0.6693 118
 Shoe Accessories 0.0000 0.0000 0.0000 4
 Shoes 0.9759 0.9799 0.9779 7323
 Skin 0.5455 0.4528 0.4948 53
 Skin Care 0.7333 0.4490 0.5570 49
 Socks 0.9417 0.9728 0.9570 698
 Sports Accessories 0.0000 0.0000 0.0000 3
 Sports Equipment 0.7083 0.8095 0.7556 21
 Stoles 0.8871 0.6111 0.7237 90
 Ties 0.9808 0.9884 0.9846 258
 Topwear 0.9822 0.9914 0.9867 15383
 Umbrellas 1.0000 1.0000 1.0000 6
 Vouchers 0.0000 0.0000 0.0000 1
 Wallets 0.9376 0.8605 0.8974 925
 Watches 0.9790 0.9921 0.9855 2542
 Water Bottle 0.0000 0.0000 0.0000 7
 Wristbands 0.0000 0.0000 0.0000 4

 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:

"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 🤗

!pip install -q transformers torch pillow gradio

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.

Base model

google/siglip2-base-patch16-224

(119)

Usage, Season, BaseColour, ArticleType, SubCategory, MasterCategory, Gender • 7 items • Updated 1 day ago • 2