https://github.com/shadmehrbakhtiary/dog-cat-classification
This project implements a Convolutional Neural Network (CNN) to classify images of dogs and cats
https://github.com/shadmehrbakhtiary/dog-cat-classification
classification cnn python tenserflow
Last synced: about 1 month ago
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This project implements a Convolutional Neural Network (CNN) to classify images of dogs and cats
- Host: GitHub
- URL: https://github.com/shadmehrbakhtiary/dog-cat-classification
- Owner: ShadmehrBakhtiary
- Created: 2024-02-03T15:21:04.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-03T19:41:39.000Z (over 2 years ago)
- Last Synced: 2025-04-05T16:35:13.998Z (about 1 year ago)
- Topics: classification, cnn, python, tenserflow
- Language: Jupyter Notebook
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Dog-Cat Image Classification using CNN
This project implements a Convolutional Neural Network (CNN) to classify images of dogs and cats. The model is trained on a high-quality dataset of dog and cat images to achieve accurate classification.
## Overview
The project uses deep learning techniques to build and train a CNN model for image classification. The model is trained on a large dataset of dog and cat images to learn and differentiate between the two classes.
## Requirements
- Python 3
- TensorFlow
- Keras
- NumPy
- Matplotlib
- Jupyter Notebook (optional, for running the code in a notebook)
## Dataset
The dataset used for training and testing the model consists of a large number of dog and cat images. Due to the high size of the dataset, it is not included in this repository.
## Model Architecture
The CNN model architecture used for image classification consists of several convolutional layers, pooling layers, and fully connected layers. The model is designed to learn and extract features from the input images to make accurate predictions.
## Training
The model is trained using the dataset, and the training process involves optimizing the model's parameters to minimize classification errors. The training process may take a significant amount of time due to the large dataset size.
## Evaluation
The trained model is evaluated on a separate test set to measure its performance in classifying unseen images of dogs and cats.
## Results
The results of the image classification model, including accuracy and performance metrics, will be presented in the project.
## Usage
To use the trained model for classifying images of dogs and cats, follow the instructions provided in the project code.