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--- |
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license: mit |
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datasets: |
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- Voxel51/FGVC-Aircraft |
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base_model: |
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- timm/tf_efficientnet_b2.in1k |
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pipeline_tag: image-classification |
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tags: |
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- aircraft |
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- airplane |
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--- |
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# Aircraft Classifier |
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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. |
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## Model Overview |
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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. |
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## Requirements |
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- **Python** 3.7 or higher |
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- **PyTorch** 1.8 or higher |
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- **torchvision** (for loading and preprocessing images) |
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## Usage |
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1. Clone this repository and install dependencies. |
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```bash |
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git clone <repository-url> |
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cd <repository-folder> |
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pip install torch torchvision |
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``` |
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2. Load and use the model in your Python script: |
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```python |
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import torch |
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from torchvision import transforms |
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from PIL import Image |
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# Load the model |
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model = torch.load('aircraft_classifier.pth') |
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model.eval() # Set to evaluation mode |
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# Load and preprocess the image |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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]) |
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img = Image.open('path_to_image.jpg') |
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img = transform(img).view(1, 3, 224, 224) # Reshape to (1, 3, 224, 224) for batch processing |
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# Predict |
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with torch.no_grad(): |
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output = model(img) |
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_, predicted = torch.max(output, 1) |
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print("Predicted Aircraft Type:", predicted.item()) |
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