Update README.md

README.md

@@ -1,3 +1,55 @@
----
-license: mit
----
+---
+license: mit
+base_model:
+- timm/tf_efficientnet_b0.in1k
+pipeline_tag: image-classification
+tags:
+- pizza
+- steak
+- sushi
+---
+# Food Classifier
+
+This repository contains a pre-trained PyTorch model for classifying food based on images. The model file `food_model.pth` can be downloaded and used to classify images of pizza, steak or sushi.
+
+## Model Overview
+
+The `food_model.pth` file is a PyTorch model trained on a dataset of food images. It achieves a test accuracy of **84.56%**, making it a reliable choice for identifying pizza, steak, and sushi. The model is designed to be lightweight and efficient for real-time applications.
+
+## Requirements
+
+- **Python** 3.7 or higher
+- **PyTorch** 1.8 or higher
+- **torchvision** (for loading and preprocessing images)
+
+## Usage
+
+1. Clone this repository and install dependencies.
+   ```bash
+   git clone <repository-url>
+   cd <repository-folder>
+   pip install torch torchvision
+    ```
+2. Load and use the model in your Python script:
+   ```python
+   import torch
+   from torchvision import transforms
+   from PIL import Image
+
+   # Load the model
+   model = torch.load('aircraft_classifier.pth')
+   model.eval()  # Set to evaluation mode
+
+   # Load and preprocess the image
+   transform = transforms.Compose([
+       transforms.Resize((224, 224)),
+       transforms.ToTensor(),
+   ])
+   img = Image.open('path_to_image.jpg')
+   img = transform(img).view(1, 3, 224, 224)  # Reshape to (1, 3, 224, 224) for batch processing
+
+   # Predict
+   with torch.no_grad():
+       output = model(img)
+       _, predicted = torch.max(output, 1)
+       print("Predicted Food Type:", predicted.item())