README.md

---
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
---

![f/n.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ZjKzw5Y6XOtCqiHsoNArs.png)

# **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
```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mrelJZ86Pt-NBce0Ty9Re.png)

---

## **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.