This project is a dog and cat classifier that uses deep learning techniques to distinguish between images of dogs and cats. It is built using TensorFlow and trained on a large dataset of dog and cat images.

• Clone the repository:

  • git clone https://github.com/Samargithubb/CNNClassifier.git

• Install the required dependencies:

  • pip install -r requirements.txt

• Run python app.py in the terminal or directly click on Run from the code editor.
• Click on the Upload button and upload the image
• After clicking on predict button
• Result will be displayed on UI

The classifier model used in this project is VGG16 based on a convolutional neural network (CNN) architecture. It has been trained on a dataset of labeled dog and cat images to learn the distinguishing features of each class. The training process involved

• Data Ingestion
• Base Model creation
• Model Training
• Model Evaluation

The classifier achieves an accuracy of ["loss": 11.801721572875977, "accuracy": 0.9830508232116699] on the test dataset. It performs well in distinguishing between images of dogs and cats, but like any machine learning model, it may occasionally make mistakes.