# 🧠 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.


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## ✨ 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

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## 🧠 Intended Uses

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

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## 🚫 Limitations

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


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## 🏋️‍♂️ 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                 |

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## 📊 Evaluation Metrics


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


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🚀 Usage
```python
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"))

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

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🗂 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

```
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🤝 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.