
![17.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/dHy-iubqlC4_rsb6xWAl-.png)

# **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**
accessory gift set baby dolls backpacks bangle basketballs bath robe beauty accessory belts blazers body lotion body wash and scrub booties boxers bra bracelet briefs camisoles capris caps casual shoes churidar clothing set clutches compact concealer cufflinks cushion covers deodorant dresses duffel bag dupatta earrings eye cream eyeshadow face moisturisers face scrub and exfoliator face serum and gel face wash and cleanser flats flip flops footballs formal shoes foundation and primer fragrance gift set free gifts gloves hair accessory hair colour handbags hat headband heels highlighter and blush innerwear vests ipad jackets jeans jeggings jewellery set jumpsuit kajal and eyeliner key chain kurta sets kurtas kurtis laptop bag leggings lehenga choli lip care lip gloss lip liner lip plumper lipstick lounge pants lounge shorts lounge tshirts makeup remover mascara mask and peel mens grooming kit messenger bag mobile pouch mufflers nail essentials nail polish necklace and chains nehru jackets night suits nightdress patiala pendant perfume and body mist rain jacket ring robe rompers rucksacks salwar salwar and dupatta sandals sarees scarves shapewear shirts shoe accessories shoe laces shorts shrug skirts socks sports sandals sports shoes stockings stoles sunglasses sunscreen suspenders sweaters sweatshirts swimwear tablet sleeve ties ties and cufflinks tights toner tops track pants tracksuits travel accessory trolley bag trousers trunk tshirts tunics umbrellas waist pouch waistcoat wallets watches water bottle wristbands