Create README.md

README.md

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+ # 🧠 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
+```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"))
+
+```
+---
+
+- 🧩 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.
+
+