---
license: mit
datasets:
- Voxel51/FGVC-Aircraft
base_model:
- timm/tf_efficientnet_b2.in1k
pipeline_tag: image-classification
tags:
- aircraft
- airplane
---
# Aircraft Classifier

This repository contains a pre-trained PyTorch model for classifying aircraft types based on images. The model file `aircraft_classifier.pth` can be downloaded and used to classify images of various aircraft models.

## Model Overview

The `aircraft_classifier.pth` file is a PyTorch model trained on a dataset of aircraft images. It achieves a test accuracy of **75.26%** on the FGVC Aircraft test dataset, making it a reliable choice for identifying aircraft types. 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 Aircraft Type:", predicted.item())