Vaibhav1772 / Flower_Classification_Using_CNN_Repo

Flower_Classification_using_CNN_in _Tensorflow

Flower_Classification_Using_CNN_Repo

Here's the complete README.md file for this GitHub repository:

Flower Classification with TensorFlow

This repository contains a flower classification model implemented using TensorFlow. The model is trained on a large dataset of flower images and can predict the species of a given flower image with high accuracy. The project demonstrates the use of convolutional neural networks (CNNs) for image classification tasks.

Key Features

• Utilizes the TensorFlow library for deep learning tasks
• Implements a CNN architecture for image classification
• Trains the model on a large dataset of flower images
• Provides data preprocessing and augmentation techniques
• Evaluates the model's performance on a separate test dataset
• Includes a trained model for direct usage or further fine-tuning
• Offers a user-friendly command-line interface for easy interaction

Installation

1. Clone the repository:

   git clone https://github.com/Vaibhav1772/Flower_Classifaction_Using_CNN_Repo.git

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Preprocess the dataset and train the model:

   python train.py

2. Use the trained model to make predictions on new flower images:

   python predict.py --image path/to/image.jpg

Dataset

The dataset used for training this model was sourced from Kaggle. Kaggle is a popular platform for data science and machine learning enthusiasts, providing a wide range of datasets for various domains.

Dataset Description

The dataset used in this project contains[different types of flower images]. It consists of [10 different species of flowers with a considerate amount of images for each species].

Accessing the Dataset

To access the dataset, follow these steps:

1. Create an account on Kaggle (if you don't have one already) at Kaggle.
2. Navigate to the dataset page: Link.
3. Click on the "Download" button to download the dataset files.

Dataset Credits

We would like to express our gratitude to the original creators of the dataset. Without their efforts in collecting, curating, and sharing the dataset, this project would not have been possible. Please refer to the Kaggle dataset page for detailed information about the dataset and the creators.

Usage

Installation

Follow these steps to set up the project and install the required dependencies:

1. Clone the repository: git clone https://github.com/Vaibhav1772/Flower_Classification_Using_CNN_Repo.git
2. Change to the project directory: cd Flower_Classification_Using_CNN_Repo
3. Install the dependencies: pip install -r requirements.txt

Training the Model

To train the model using the dataset, run the following command:

python train.py

## Contributing

Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request. Make sure to follow the repository's code of conduct and contribution guidelines.

## License

This project is licensed under the MIT License.

## Acknowledgements

- The flower dataset used in this project is sourced from [Kaggle](https://www.kaggle.com/).
- Special thanks to the creators and contributors of TensorFlow and other open-source libraries used in this project.

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