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| license: mit |
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| # EfficientNet Model for AI Data Classification using PyTorch |
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| This is a machine learning model trained on AI data using the EfficientNet architecture in PyTorch. The model achieved an accuracy of 97.314% on the validation set. |
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| ## Requirements |
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| - Python 3.x |
| - PyTorch |
| - torchvision |
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| ## Usage |
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| To use the model, simply import it and load the weights: |
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| ```python |
| import torch |
| from efficientnet_model import EfficientNetModel |
| |
| # create an instance of the model |
| model = EfficientNetModel() |
| |
| # load the weights |
| model.load_state_dict(torch.load("path/to/weights.pth")) |
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| Once the weights are loaded, you can use the model for inference on new data: |
| |
| # load your data |
| data = ... |
| |
| # preprocess the data |
| preprocessed_data = ... |
| |
| # move the data to GPU if available |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| preprocessed_data = preprocessed_data.to(device) |
| |
| # make a prediction |
| with torch.no_grad(): |
| prediction = model(preprocessed_data) |
| |
| # print the predicted class |
| predicted_class = torch.argmax(prediction, dim=1) |
| print(predicted_class) |
| |
| Training |
| The model was trained on a labeled dataset of AI data using the EfficientNet architecture in PyTorch. The data was split into a training set and a validation set, with a 80/20 split. The model was trained using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss. |
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| To train the model on your own dataset, you can use the train.py script provided in this repository. Simply modify the script to load your own data and configure the training parameters. |
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| Evaluation |
| The model achieved an accuracy of 97.314% on the validation set. To reproduce this result, you can use the evaluate.py script provided in this repository. Simply modify the script to load your own data and configure the evaluation parameters. |
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