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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:
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 🤗**
```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** |