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+# Food-101 Image Classification with EfficientNet
+
+This project focuses on fine-tuning the **EfficientNet-B0** model for image classification using the **Food-101** dataset. The model is trained to classify images into 101 different food categories.
+
+## Model Summary
+
+- **Model Architecture**: EfficientNet-B0
+- **Dataset**: Food-101
+- **Number of Classes**: 101
+- **Input Image Size**: 224x224
+- **Normalization**: Mean - `[0.485, 0.456, 0.406]`, Std - `[0.229, 0.224, 0.225]`
+- **Optimizer**: Adam
+- **Loss Function**: CrossEntropyLoss
+- **Learning Rate**: 0.001
+- **Epochs**: 10
+
+## Dataset
+
+The **Food-101** dataset consists of 101 food categories, with 1,000 images for each category. The dataset is already split into training (75,750 images) and validation (25,250 images).
+
+- Dataset link: [Food-101 on Hugging Face](https://huggingface.co/datasets/ethz/food101)
+
+## Model Training
+
+The model is based on **EfficientNet-B0**, a state-of-the-art architecture for image classification. The pretrained model is fine-tuned on the Food-101 dataset using PyTorch.
+
+### Training Process
+
+- Images are resized to 256x256 and center-cropped to 224x224.
+- Data augmentation techniques such as random horizontal flip and random rotation were applied to increase the variety of training images.
+- The Adam optimizer was used with a learning rate of 0.001, and cross-entropy loss was used for classification.
+
+
+## Model Performance
+- Loss: 1.0926
+- Validation Accuracy: 0.6944
+- Test Accuracy: 0.8088
+
+These metrics were achieved after 10 epochs of training
+
+## How to use the model
+You can use the model directly in your own projects by loading it from hugging face.
+
+### Requirements 
+- Python 3.7+
+- PyTorch 1.9+
+- torchvision 0.10+
+- PIL (Pillow)
+- Hugging Face transformers library
+
+## Model Card
+- ModelType: EfficientNet-B0
+- Dataset: Food-101
+- Framework: Pytorch
+- Optimized for: Image Classification