Cat's and Dogs Image Recognition with Convolutional Neural Networks (CNN) Introduction This repository contains the code and resources for building a Convolutional Neural Network (CNN) to classify images of cats and dogs. The model is trained using deep learning techniques to distinguish between images of cats and dogs with high accuracy.
Dataset The dataset used for training and testing the model consists of a large number of images of cats and dogs. The dataset is divided into two subsets: one for training the model and another for evaluating its performance. The dataset can be obtained from
Installation To run the code in this repository, you will need Python 3.x and the following libraries:
TensorFlow Keras NumPy Matplotlib [Any other libraries used in the project] You can install these dependencies using pip:
css Copy code pip install tensorflow keras numpy matplotlib [other libraries]
Model Architecture The CNN model architecture used in this project consists of several convolutional layers followed by max-pooling layers to extract features from the input images. These features are then fed into fully connected layers to make predictions. The model is trained using the Adam optimizer with a categorical cross-entropy loss function. The final layer uses a softmax activation function to output probabilities for each class (cat or dog).
Results After training the model, it achieved a validation accuracy of [accuracy] on the test dataset. The model performs well in classifying images of cats and dogs, achieving high accuracy and generalizing well to unseen data.
Future Work There are several ways in which this project could be extended or improved:
Experiment with different CNN architectures to improve accuracy, such as adding more convolutional layers or using different activation functions. Augment the dataset with additional images to make the model more robust, including images with different backgrounds, lighting conditions, and poses. Deploy the model as a web service or mobile application for real-time image classification, allowing users to classify their own images of cats and dogs.