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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.

Classification Report:
 precision recall f1-score support

 Accessory Gift Set 0.9898 1.0000 0.9949 97
 Baby Dolls 0.6667 0.1429 0.2353 14
 Backpacks 0.9582 0.9503 0.9542 724
 Bangle 0.8421 0.7529 0.7950 85
 Basketballs 0.7500 0.9231 0.8276 13
 Bath Robe 0.8571 0.7059 0.7742 17
 Beauty Accessory 0.0000 0.0000 0.0000 3
 Belts 0.9842 0.9938 0.9890 813
 Blazers 0.8333 0.6250 0.7143 8
 Body Lotion 1.0000 0.3333 0.5000 3
 Body Wash and Scrub 0.0000 0.0000 0.0000 1
 Booties 0.6875 0.9167 0.7857 12
 Boxers 0.8679 0.8846 0.8762 52
 Bra 0.9614 0.9916 0.9763 477
 Bracelet 0.7656 0.7424 0.7538 66
 Briefs 0.9731 0.9811 0.9771 847
 Camisoles 0.7500 0.5385 0.6269 39
 Capris 0.6558 0.8057 0.7231 175
 Caps 0.9317 0.9647 0.9479 283
 Casual Shoes 0.8338 0.8643 0.8488 2845
 Churidar 0.7500 0.5000 0.6000 30
 Clothing Set 0.7500 0.3750 0.5000 8
 Clutches 0.8015 0.7431 0.7712 288
 Compact 0.8864 1.0000 0.9398 39
 Concealer 0.7143 0.9091 0.8000 11
 Cufflinks 0.9811 0.9811 0.9811 106
 Cushion Covers 0.0000 0.0000 0.0000 1
 Deodorant 0.8946 0.9539 0.9233 347
 Dresses 0.7956 0.8642 0.8285 464
 Duffel Bag 0.8947 0.5795 0.7034 88
 Dupatta 0.9008 0.9397 0.9198 116
 Earrings 0.9952 0.9880 0.9916 416
 Eye Cream 1.0000 0.2500 0.4000 4
 Eyeshadow 0.9062 0.9062 0.9062 32
 Face Moisturisers 0.5846 0.8085 0.6786 47
Face Scrub and Exfoliator 0.0000 0.0000 0.0000 4
 Face Serum and Gel 0.0000 0.0000 0.0000 2
 Face Wash and Cleanser 0.6667 0.6250 0.6452 16
 Flats 0.5764 0.2640 0.3621 500
 Flip Flops 0.8573 0.9464 0.8996 914
 Footballs 1.0000 0.3750 0.5455 8
 Formal Shoes 0.8246 0.8932 0.8576 637
 Foundation and Primer 0.9524 0.8696 0.9091 69
 Fragrance Gift Set 0.6842 0.9123 0.7820 57
 Free Gifts 0.9000 0.0989 0.1782 91
 Gloves 0.9375 0.7500 0.8333 20
 Hair Accessory 0.0000 0.0000 0.0000 1
 Hair Colour 0.8636 1.0000 0.9268 19
 Handbags 0.8840 0.9744 0.9270 1759
 Hat 0.0000 0.0000 0.0000 3
 Headband 1.0000 0.5714 0.7273 7
 Heels 0.7622 0.9206 0.8340 1323
 Highlighter and Blush 0.9697 0.8421 0.9014 38
 Innerwear Vests 0.9056 0.8719 0.8884 242
 Ipad 0.0000 0.0000 0.0000 1
 Jackets 0.7950 0.6163 0.6943 258
 Jeans 0.8118 0.9385 0.8706 602
 Jeggings 1.0000 0.0882 0.1622 34
 Jewellery Set 0.9333 0.9655 0.9492 58
 Jumpsuit 0.0000 0.0000 0.0000 16
 Kajal and Eyeliner 0.7241 0.8936 0.8000 94
 Key chain 0.0000 0.0000 0.0000 2
 Kurta Sets 0.8774 0.9894 0.9300 94
 Kurtas 0.9348 0.9414 0.9381 1844
 Kurtis 0.5000 0.5427 0.5205 234
 Laptop Bag 0.6338 0.5488 0.5882 82
 Leggings 0.7590 0.8362 0.7957 177
 Lehenga Choli 0.0000 0.0000 0.0000 4
 Lip Care 0.8000 0.5714 0.6667 7
 Lip Gloss 0.8718 0.9358 0.9027 109
 Lip Liner 0.8846 0.5111 0.6479 45
 Lip Plumper 1.0000 0.5000 0.6667 4
 Lipstick 0.9660 0.9846 0.9752 260
 Lounge Pants 0.7727 0.2787 0.4096 61
 Lounge Shorts 1.0000 0.1176 0.2105 34
 Lounge Tshirts 0.5000 0.6667 0.5714 3
 Makeup Remover 0.0000 0.0000 0.0000 2
 Mascara 0.6000 0.5000 0.5455 12
 Mask and Peel 0.7778 0.7000 0.7368 10
 Mens Grooming Kit 0.0000 0.0000 0.0000 1
 Messenger Bag 0.6818 0.3409 0.4545 44
 Mobile Pouch 0.5714 0.5106 0.5393 47
 Mufflers 0.8056 0.7632 0.7838 38
 Nail Essentials 1.0000 0.5000 0.6667 6
 Nail Polish 0.9928 0.9964 0.9946 278
 Necklace and Chains 0.9375 0.9375 0.9375 160
 Nehru Jackets 0.0000 0.0000 0.0000 5
 Night suits 0.8792 0.9291 0.9034 141
 Nightdress 0.7730 0.7606 0.7668 188
 Patiala 1.0000 0.7368 0.8485 38
 Pendant 0.9181 0.8920 0.9049 176
 Perfume and Body Mist 0.9463 0.9055 0.9254 603
 Rain Jacket 0.0000 0.0000 0.0000 7
 Ring 0.8952 0.9407 0.9174 118
 Robe 0.0000 0.0000 0.0000 4
 Rompers 1.0000 1.0000 1.0000 12
 Rucksacks 0.7143 0.4545 0.5556 11
 Salwar 0.6122 0.9375 0.7407 32
 Salwar and Dupatta 1.0000 0.8571 0.9231 7
 Sandals 0.8618 0.8291 0.8451 895
 Sarees 0.9660 0.9977 0.9816 427
 Scarves 0.8333 0.7983 0.8155 119
 Shapewear 0.2500 0.1111 0.1538 9
 Shirts 0.9360 0.9614 0.9485 3212
 Shoe Accessories 0.0000 0.0000 0.0000 3
 Shoe Laces 0.0000 0.0000 0.0000 1
 Shorts 0.8986 0.9232 0.9107 547
 Shrug 0.0000 0.0000 0.0000 6
 Skirts 0.8293 0.7969 0.8127 128
 Socks 0.9869 0.9883 0.9876 686
 Sports Sandals 0.6111 0.1642 0.2588 67
 Sports Shoes 0.8880 0.8100 0.8472 2016
 Stockings 0.8824 0.9375 0.9091 32
 Stoles 0.8690 0.8111 0.8391 90
 Sunglasses 0.9898 0.9991 0.9944 1073
 Sunscreen 1.0000 0.7333 0.8462 15
 Suspenders 1.0000 1.0000 1.0000 40
 Sweaters 0.7488 0.5812 0.6545 277
 Sweatshirts 0.6348 0.7930 0.7051 285
 Swimwear 0.9000 0.5294 0.6667 17
 Tablet Sleeve 0.0000 0.0000 0.0000 3
 Ties 1.0000 0.9886 0.9943 263
 Ties and Cufflinks 0.0000 0.0000 0.0000 2
 Tights 1.0000 0.3333 0.5000 9
 Toner 0.0000 0.0000 0.0000 2
 Tops 0.7591 0.7208 0.7394 1762
 Track Pants 0.8537 0.8257 0.8395 304
 Tracksuits 0.8750 0.9655 0.9180 29
 Travel Accessory 1.0000 0.1875 0.3158 16
 Trolley Bag 0.0000 0.0000 0.0000 3
 Trousers 0.9428 0.8396 0.8882 530
 Trunk 0.8819 0.9071 0.8944 140
 Tshirts 0.9273 0.9580 0.9424 7065
 Tunics 0.6129 0.1659 0.2612 229
 Umbrellas 1.0000 1.0000 1.0000 6
 Waist Pouch 1.0000 0.1176 0.2105 17
 Waistcoat 1.0000 0.2667 0.4211 15
 Wallets 0.9491 0.9235 0.9361 928
 Watches 0.9817 0.9929 0.9873 2542
 Water Bottle 1.0000 0.8182 0.9000 11
 Wristbands 0.8571 0.8571 0.8571 7

 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 🤗

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-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

Base model

google/siglip2-base-patch16-224

(119)

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