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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:
- gender
- fashion
- product
---
# **Fashion-Product-Gender**
> **Fashion-Product-Gender** is a vision model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images into one of five gender categories.
```py
Classification Report:
precision recall f1-score support
Boys 0.4127 0.0940 0.1531 830
Girls 0.5000 0.0061 0.0121 655
Men 0.7506 0.8393 0.7925 22104
Unisex 0.5714 0.0188 0.0364 2126
Women 0.7317 0.7609 0.7460 18357
accuracy 0.7407 44072
macro avg 0.5933 0.3438 0.3480 44072
weighted avg 0.7240 0.7407 0.7130 44072
```
The model predicts one of the following gender categories for fashion products:
- **0:** Boys
- **1:** Girls
- **2:** Men
- **3:** Unisex
- **4:** Women
---
# **Run with Transformers 🤗**
```python
!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-Gender" # Replace with your actual model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
0: "Boys",
1: "Girls",
2: "Men",
3: "Unisex",
4: "Women"
}
def classify_gender(image):
"""Predicts the gender category 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_gender,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Gender Prediction Scores"),
title="Fashion-Product-Gender",
description="Upload a fashion product image to predict the target gender category (Boys, Girls, Men, Unisex, Women)."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
---
# **Intended Use**
This model is best suited for:
- **Fashion E-commerce tagging and search**
- **Personalized recommendations based on gender**
- **Catalog organization and gender-based filters**
- **Retail analytics and demographic insights** |