This project utilizes Convolutional Neural Networks (CNN) for the classification of flower images into categories, including tulip, sunflower, roses, dandelions, and daisy.
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Technology Stack:
- Implemented using Python and TensorFlow/Keras.
- Leveraged CNN architecture for effective image classification.
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Dataset:
- Used a diverse dataset containing images of tulips, sunflowers, roses, dandelions, and daisies.
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Model Training:
- Trained the model on a specified architecture to achieve accurate flower classification.
- Achieved competitive results in terms of accuracy and performance.
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Usage:
- Easily deployable for image classification tasks.
- Instructions provided for training the model on your dataset.
- Clone the repository:
- git clone https://github.com/selcia25/flower-image-classification.git
- cd flower-image-classification