https://github.com/adilshamim8/cat_vs_dog_image_classification_project
The Cat vs Dog Image Classification Project is a machine learning initiative that employs a convolutional neural network (CNN) to automatically classify images as either a cat or a dog using advanced deep learning techniques.
https://github.com/adilshamim8/cat_vs_dog_image_classification_project
Last synced: 2 months ago
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The Cat vs Dog Image Classification Project is a machine learning initiative that employs a convolutional neural network (CNN) to automatically classify images as either a cat or a dog using advanced deep learning techniques.
- Host: GitHub
- URL: https://github.com/adilshamim8/cat_vs_dog_image_classification_project
- Owner: AdilShamim8
- Created: 2024-10-28T13:51:24.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-03T10:11:45.000Z (3 months ago)
- Last Synced: 2025-03-03T11:25:43.801Z (3 months ago)
- Language: Jupyter Notebook
- Size: 350 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Cat vs Dog Image Classification Project
## Description
The Cat vs Dog Image Classification Project is a machine learning initiative aimed at building an automated system capable of accurately classifying images as either featuring a cat or a dog. Leveraging state-of-the-art deep learning techniques, this project utilizes a convolutional neural network (CNN) architecture to distinguish between these two popular pet species based on visual input.
### Features
- **Dataset**: The project is built on a curated dataset comprising thousands of labeled images of cats and dogs, allowing the model to learn from diverse examples.
- **Model Architecture**: A custom CNN model is designed and trained to extract relevant features from images, enhancing classification accuracy.
- **Data Preprocessing**: Various preprocessing techniques are applied, including normalization, augmentation, and resizing, to improve model robustness.
- **Training and Validation**: The model is trained using a split of training and validation datasets to ensure generalization and prevent overfitting.
- **Performance Metrics**: The project evaluates model performance using metrics such as accuracy, precision, recall, and F1 score.
- **Visualization**: Includes tools for visualizing training progress, metrics, and confusion matrices for comprehensive analysis.
### Objectives
- To develop a reliable image classification model that can differentiate between cat and dog images with high accuracy.
- To explore various deep learning techniques and hyperparameter optimization to enhance model performance.
- To provide a well-documented codebase for educational purposes and future enhancements.