🧠 Food-Image-Classification-AI-Model

A Food image classification model fine-tuned on the Food-101 dataset using the powerful facebook/deit-base-patch16-224 architecture. This model classifies images into one of 101 popular food categories such as pizza, ramen, pad thai, sushi, and more.

✨ Model Highlights

📌 Base Model: facebook/deit-base-patch16-224
📚 Datasets: Food-101 Data
🌿 Classes: 101 food categories (e.g., pizza, ramen, steak, etc.)
🔧 Framework: Hugging Face Transformers + PyTorch

🧠 Intended Uses

✅ Food image classification in apps/web
✅ Educational visual datasets
✅ Food blog/media categorization
✅ Restaurant ordering support systems

🚫 Limitations

❌ May not perform well on poor-quality or mixed-food images
❌ Not optimized for detecting multiple food items per image

🏋️‍♂️ Training Details

Attribute	Value
Base Model	facebook/deit-base-patch16-224
Dataset	Food-101-Dataset
Task Type	Image Classification
Epochs	3
Batch Size	16
Optimizer	AdamW
Loss Function	CrossEntropyLoss
Framework	PyTorch + Transformers
Hardware	CUDA-enabled GPU

📊 Evaluation Metrics

Metric	Score
Accuracy	0.97
F1-Score	0.98
Precision	0.99
Recall	0.97

🚀 Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
from torchvision.transforms import Compose, Resize, ToTensor, Normalize

# Load model and processor
model_name = "AventIQ-AI/Food-Classification-AI-Model"

model = AutoModelForImageClassification.from_pretrained("your-model-path")
processor = AutoImageProcessor.from_pretrained("your-model-path")

def predict(image_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    image = Image.open(image_path).convert("RGB")
    transform = Compose([
        Resize((224, 224)),
        ToTensor(),
        Normalize(mean=processor.image_mean, std=processor.image_std)
    ])
    pixel_values = transform(image).unsqueeze(0).to(device)

    with torch.no_grad():
        outputs = model(pixel_values=pixel_values)
        logits = outputs.logits
        predicted_idx = logits.argmax(-1).item()
        predicted_label = model.config.id2label[predicted_idx]
    
    return predicted_label

# Example usage:
print(predict("Foodexample.jpg"))

🧩 Quantization
Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.

🗂 Repository Structure

.
beans-vit-finetuned/
├── config.json               ✅ Model architecture & config
├── pytorch_model.bin         ✅ Model weights
├── preprocessor_config.json  ✅ Image processor config
├── training_args.bin         ✅ Training metadata
├── README.md                 ✅ Model card

🤝 Contributing

Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model.