README.md

metadata

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.

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

pip install -q transformers torch pillow gradio

---

Inference Code

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.