👁 fffffff.png

Indian-Western-Food-34

Indian-Western-Food-34 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify food images into various Indian and Western dishes using the SiglipForImageClassification architecture.

Classification Report:
 precision recall f1-score support

 Baked Potato 0.9912 0.9780 0.9846 1500
Crispy Chicken 0.9811 0.9707 0.9759 1500
 Donut 0.9893 0.9893 0.9893 1500
 Fries 0.9742 0.9827 0.9784 1500
 Hot Dog 0.9830 0.9735 0.9783 1548
 Sandwich 0.9898 0.9673 0.9784 1500
 Taco 0.9327 0.9427 0.9377 1500
 Taquito 0.9624 0.9387 0.9504 1500
 Apple Pie 0.9666 0.9540 0.9602 1000
 Burger 0.9114 0.9940 0.9509 331
 Butter Naan 0.9691 0.9186 0.9431 307
 Chai 0.9801 1.0000 0.9899 344
 Chapati 0.9188 0.9694 0.9435 327
 Cheesecake 0.9573 0.9640 0.9606 1000
 Chicken Curry 0.9610 0.9850 0.9728 1000
 Chole Bhature 0.9841 0.9867 0.9854 376
 Dal Makhani 0.9698 0.9797 0.9747 295
 Dhokla 0.9959 0.9959 0.9959 245
 Fried Rice 0.9485 1.0000 0.9736 350
 Ice Cream 0.9569 0.9770 0.9668 1000
 Idli 0.9934 1.0000 0.9967 302
 Jalebi 0.9931 1.0000 0.9965 288
 Kaathi Rolls 0.9640 0.9606 0.9623 279
 Kadai Paneer 0.9848 0.9731 0.9789 334
 Kulfi 0.9810 0.9673 0.9741 214
 Masala Dosa 0.9890 0.9890 0.9890 273
 Momos 0.9908 0.9969 0.9938 323
 Omelette 0.9829 0.9790 0.9810 1000
 Paani Puri 0.9281 0.9861 0.9562 144
 Pakode 0.9738 0.9665 0.9701 269
 Pav Bhaji 0.9901 0.9803 0.9852 305
 Pizza 0.9647 0.9927 0.9785 275
 Samosa 0.9878 0.9959 0.9918 244
 Sushi 0.9969 0.9800 0.9884 1000

 accuracy 0.9729 23873
 macro avg 0.9719 0.9775 0.9745 23873
 weighted avg 0.9731 0.9729 0.9729 23873

---

The model categorizes images into 34 food classes:

Western Foods

• Class 0: "Baked Potato"
• Class 1: "Crispy Chicken"
• Class 2: "Donut"
• Class 3: "Fries"
• Class 4: "Hot Dog"
• Class 5: "Sandwich"
• Class 6: "Taco"
• Class 7: "Taquito"
• Class 8: "Apple Pie"
• Class 9: "Burger"
• Class 13: "Cheesecake"
• Class 18: "Fried Rice"
• Class 19: "Ice Cream"
• Class 27: "Omelette"
• Class 31: "Pizza"
• Class 33: "Sushi"

Indian Foods

• Class 10: "Butter Naan"
• Class 11: "Chai"
• Class 12: "Chapati"
• Class 14: "Chicken Curry"
• Class 15: "Chole Bhature"
• Class 16: "Dal Makhani"
• Class 17: "Dhokla"
• Class 20: "Idli"
• Class 21: "Jalebi"
• Class 22: "Kaathi Rolls"
• Class 23: "Kadai Paneer"
• Class 24: "Kulfi"
• Class 25: "Masala Dosa"
• Class 26: "Momos"
• Class 28: "Paani Puri"
• Class 29: "Pakode"
• Class 30: "Pav Bhaji"
• Class 32: "Samosa"

---

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Indian-Western-Food-34"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def food_classification(image):
 """Predicts the type of food in an 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()
 
 labels = {
 "0": "Baked Potato", "1": "Crispy Chicken", "2": "Donut", "3": "Fries", 
 "4": "Hot Dog", "5": "Sandwich", "6": "Taco", "7": "Taquito", "8": "Apple Pie", 
 "9": "Burger", "10": "Butter Naan", "11": "Chai", "12": "Chapati", "13": "Cheesecake", 
 "14": "Chicken Curry", "15": "Chole Bhature", "16": "Dal Makhani", "17": "Dhokla", 
 "18": "Fried Rice", "19": "Ice Cream", "20": "Idli", "21": "Jalebi", "22": "Kaathi Rolls", 
 "23": "Kadai Paneer", "24": "Kulfi", "25": "Masala Dosa", "26": "Momos", "27": "Omelette", 
 "28": "Paani Puri", "29": "Pakode", "30": "Pav Bhaji", "31": "Pizza", "32": "Samosa", 
 "33": "Sushi"
 }
 predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
 
 return predictions

# Create Gradio interface
iface = gr.Interface(
 fn=food_classification,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(label="Prediction Scores"),
 title="Indian & Western Food Classification",
 description="Upload a food image to classify it into one of the 34 food types."
)

# Launch the app
if __name__ == "__main__":
 iface.launch()

---

Intended Use:

The Indian-Western-Food-34 model is designed to classify food images into Indian and Western dishes. Potential use cases include:

• Restaurant & Food Delivery Apps: Enhancing food recognition for better menu recommendations.
• Health & Nutrition Apps: Tracking calorie intake and diet preferences.
• Food Blogging & Social Media: Auto-tagging food items in posts.
• Educational Purposes: Teaching AI-based food classification.

Base model

google/siglip2-base-patch16-224

(119)

Age, Gender, Race and More .. • 10 items • Updated 1 day ago • 2