This project aims to classify images as either containing a dog or a cat using machine learning techniques. The classification task is based on image recognition, where the model learns to differentiate between features specific to dogs and cats.
The dataset consists of a large number of images of both dogs and cats. Each image is labeled as either a dog or a cat. The dataset is divided into training and testing sets to train and evaluate the model's performance.
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Data Preprocessing: Images are resized to a consistent size to ensure uniformity. Additionally, data augmentation techniques such as rotation, flipping, and zooming may be applied to increase the diversity of the training dataset.
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Model Selection: Various machine learning algorithms and deep learning architectures can be employed for this task. Common choices include Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in image classification tasks.
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Training: The selected model is trained using the training dataset. During training, the model learns to extract features from the images and make predictions based on those features. The training process involves adjusting the model's parameters to minimize the prediction error.
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Evaluation: The trained model is evaluated using the testing dataset to assess its performance and generalization ability. Metrics such as accuracy, precision, recall, and F1-score are commonly used to measure the model's effectiveness.
- Python 3.x
- TensorFlow or PyTorch (for deep learning)
- Scikit-learn (for machine learning algorithms)
- OpenCV (for image processing)
- Matplotlib and/or Seaborn (for data visualization)
- Jupyter Notebook or any Python IDE
- Clone the repository or download the project files.
- Install the required dependencies using pip or conda.
- Run the Jupyter Notebook or Python script to train the model and evaluate its performance.
- Make predictions on new images using the trained model.
Contributions to the project are welcome! Whether it's improving the model's performance, adding new features, or optimizing the code, feel free to submit pull requests.
This project was developed by Anirudh Varshney.