This project is a collaborative notebook that uses deep learning techniques to identify dog breeds from images. The notebook is built using Python and the popular deep learning framework, TensorFlow. It is a great resource for those interested in computer vision and deep learning, as it demonstrates how to build a deep learning model from scratch to identify dog breeds.
To run this project, you will need to have access to Google Colab, a free Jupyter notebook environment that allows you to write and run Python code in the cloud. You can access it by signing in with your Google account here.
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To start using the notebook, follow these steps:
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Open the Google Colab notebook by clicking on the
dog_breed_detection.ipynbfile in the repository. -
Click the 'Open in Colab' button to open the notebook in Google Colab.
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Follow the instructions in the notebook to load the dataset, preprocess the images, and build a deep learning model to identify dog breeds.
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Run the cells in the notebook to train the model and evaluate its performance on a test set of images.
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Experiment with different hyperparameters and model architectures to improve the model's performance.
This project is licensed under the MIT License - see the LICENSE file for details.
This project was inspired by the challenges of computer vision and deep learning and built using the knowledge gained from learning Python, TensorFlow, and computer vision techniques.