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---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-so400m-patch14-384
pipeline_tag: image-classification
library_name: transformers
tags:
- fashion
- product
- season
- siglip2
- image
- classification
---
# **Fashion-Product-Season**
> **Fashion-Product-Season** is a vision-language model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images based on their suitable **season of use**.
```py
Classification Report:
precision recall f1-score support
Fall 0.6173 0.5655 0.5903 11414
Spring 0.9738 0.7665 0.8578 2711
Summer 0.7051 0.8107 0.7542 21438
Winter 0.8007 0.6432 0.7134 8509
accuracy 0.7121 44072
macro avg 0.7742 0.6965 0.7289 44072
weighted avg 0.7174 0.7121 0.7103 44072
```
The model predicts one of the following **season categories**:
- **0:** Fall
- **1:** Spring
- **2:** Summer
- **3:** Winter
---
# **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-Season" # Replace with your actual model path
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
0: "Fall",
1: "Spring",
2: "Summer",
3: "Winter"
}
def classify_season(image):
"""Predicts the most suitable season 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))}
predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True))
return predictions
# Gradio interface
iface = gr.Interface(
fn=classify_season,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Season Prediction Scores"),
title="Fashion-Product-Season",
description="Upload a fashion product image to predict its most suitable season (Fall, Spring, Summer, Winter)."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
---
# **Intended Use**
This model can be used for:
- **Seasonal tagging in fashion e-commerce**
- **Improved recommendations for seasonal shopping trends**
- **Inventory planning based on product seasonality**
- **Data labeling for fashion-specific recommendation engines** |