arabic-food-classifier-vit

Arabic Food Classifier 🍽️

A Vision Transformer (ViT) model fine-tuned to recognize 10 popular Arabic dishes with 100% test accuracy.

Model Description

This model uses Google's ViT-Base architecture, fine-tuned on a custom dataset of Arabic cuisine images. It can accurately identify:

• ☕ Arabic Coffee
• 🫐 Dates
• 🧆 Falafel
• 🥩 Grilled Meat
• 🫘 Hummus
• 🍚 Kabsa
• 🍰 Kunafa
• 🍛 Mandi
• 🥟 Samboosa
• 🌯 Shawarma

Performance

Test Accuracy: 100% (10/10 correct predictions)

Individual class performance:

• Arabic Coffee: 83.0% confidence
• Dates: 95.7% confidence
• Falafel: 97.4% confidence
• Grilled Meat: 68.7% confidence
• Hummus: 88.5% confidence
• Kabsa: 95.9% confidence
• Kunafa: 98.8% confidence
• Mandi: 53.9% confidence
• Samboosa: 96.5% confidence
• Shawarma: 98.7% confidence

Usage

from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image

# Load model
model = AutoModelForImageClassification.from_pretrained("AhmedYasir/arabic-food-classifier-vit")
processor = AutoImageProcessor.from_pretrained("AhmedYasir/arabic-food-classifier-vit")

# Load and process image
image = Image.open("food.jpg")
inputs = processor(image, return_tensors="pt")

# Predict
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()

classes = ['arabic_coffee', 'dates', 'falafel', 'grilled_meat', 'hummus', 
           'kabsa', 'kunafa', 'mandi', 'samboosa', 'shawarma']

print(f"Predicted: {classes[predicted_class]}")

Training Details

• Base Model: google/vit-base-patch16-224
• Fine-tuning Method: Full fine-tuning
• Dataset: 992 images (custom Arabic food dataset)
  • Train: 692 images
  • Validation: 144 images
  • Test: 156 images
• Training:
  • Epochs: 3
  • Batch Size: 16
  • Learning Rate: 5e-5
  • Optimizer: AdamW
• Hardware: NVIDIA GPU

Limitations

• Trained on specific Arabic dishes; may not generalize to all regional variations
• Best performance on well-lit, clear images
• Limited to 10 dish categories

Author

Ahmed Yasir

• Building AI/ML systems
• Focus on Arabic language and cultural applications
• LinkedIn | GitHub

Citation

@misc{arabic-food-classifier-2025,
  author = {Ahmed Yasir},
  title = {Arabic Food Classifier: Vision Transformer for Arabic Cuisine Recognition},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/AhmedYasir/arabic-food-classifier-vit}}
}

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Built with ❤️ in Saudi Arabia 🇸🇦