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license: apache-2.0
datasets:
- avnishs17/food_not_food
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
- food
- biology
- Food-or-Not
---
# **Food-or-Not-SigLIP2**
> **Food-or-Not-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to distinguish between images of **food** and **non-food** objects using the **SiglipForImageClassification** architecture.
```py
Classification Report:
precision recall f1-score support
food 0.8902 0.8610 0.8753 4000
not-food 0.8654 0.8938 0.8794 4000
accuracy 0.8774 8000
macro avg 0.8778 0.8774 0.8773 8000
weighted avg 0.8778 0.8774 0.8773 8000
```
---
## **Label Space: 2 Classes**
The model classifies each image into one of the following categories:
```
Class 0: "food"
Class 1: "not-food"
```
---
## **Install Dependencies**
```bash
pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Food-or-Not-SigLIP2" # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "food",
"1": "not-food"
}
def classify_food(image):
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()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_food,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Food Classification"),
title="Food-or-Not-SigLIP2",
description="Upload an image to detect if it contains food or not."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Intended Use**
**Food-or-Not-SigLIP2** can be used for:
* **Dietary Apps** – Automatically classify images for food detection.
* **Retail & E-commerce** – Filter food vs non-food products visually.
* **Content Moderation** – Flag content containing food items.
* **Dataset Curation** – Separate food-related images for training or filtering. |