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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

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Label Space: 2 Classes

The model classifies each image into one of the following categories:

Class 0: "food"
Class 1: "not-food"

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Install Dependencies

pip install -q transformers torch pillow gradio

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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()

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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.

Base model

google/siglip2-base-patch16-224

(119)

new exps • 3 items • Updated 1 day ago • 1