#Image Classification with TensorFlow
Description:
This project tackles the task of image classification using the powerful capabilities of TensorFlow. It leverages deep learning architectures to accurately identify and categorize objects within images.
Key Features:
- TensorFlow Integration: Seamlessly utilizes TensorFlow's libraries and functionalities to build and train a robust image classification model.
- Customizable Architecture: The code allows for flexibility in choosing or designing the neural network architecture that best suits your specific classification problem.
- Data Preprocessing: Implements essential image preprocessing techniques like resizing, normalization, and data augmentation to enhance model performance.
- Training and Evaluation: Provides a well-structured training process with clear loss visualization and evaluation metrics to assess the model's effectiveness.
- We are gonna be using the Convolutional Neural Network here.
Getting Started:
Prerequisites:
- Ensure you have Python (version 3. x recommended) and TensorFlow installed. You can install them using pip install tensorflow.
- At least you have the basic knowledge of the CNN model(like where to implement it, how does it work).For your help i aslo write the comments so that you can refer to the code comments or documentation for specific requirements.
Project Setup:
Clone this repository using git clone https://your_repository_url.git. Create a virtual environment (recommended) to isolate project dependencies: python -m venv venv and source venv/bin/activate.
Data Preparation:
Please prepare your image dataset, making sure it's well-organized into labeled folders or using a suitable format for TensorFlow's data loading functions. Adjust the data paths in the code to point to your dataset location.
Customization:
Experiment with different neural network architectures by modifying the model definition section in the code. Explore various data augmentation techniques to improve model robustness. To optimize performance, Fine-tune hyperparameters like learning rate, batch size, and optimizer settings.
Contact: You can contact me through my email - id = dhruvsharma4054@gmail.com
Feel free to reach out if you have any questions or suggestions about this project.
Finally, if you like my work, make sure to enjoy the repository and follow me for more informational content.