This project is about classifying the images of dogs & cats from kaggle dataset. The specific tools used in the project might depend on the programming language and chosen libraries, but here's a general breakdown of the typical tools involved in an SVM image classification project for cats vs. dogs:
- Programming Language:
Python: It's a popular choice due to its extensive machine learning libraries and ease of use.
- Libraries:
-Scikit-learn: This is a popular Python library that provides various machine learning algorithms, including SVM. It offers functionalities for data loading, preprocessing, model training, evaluation, and prediction. -Image processing library (Optional): Depending on the chosen feature extraction method, libraries like OpenCV or Pillow might be used for loading, manipulating, and extracting features from images. -NumPy: This library provides powerful tools for numerical computations, essential for handling image data as arrays. -Matplotlib (Optional): This library is useful for data visualization, which can help analyze the extracted features or visualize the model's performance.
- Dataset:
A dataset containing labeled images of cats and dogs. There are many publicly available datasets suitable for this task, such as CIFAR-100 or MNIST.
- Additional Tools:
Google Colab: A cloud-based platform that allows running Python notebooks in the browser without local machine setup.
These are the core tools typically used for building an SVM image classifier for cats vs. dogs.