https://github.com/phenomsg/cat-dog-classifier

A Python TensorFlow model classifying cat and dog images using deep learning techniques for accurate and efficient image recognition.
https://github.com/phenomsg/cat-dog-classifier

binaryclassification jupyter-notebook opencv python3 tensorflow2

Last synced: 10 days ago
JSON representation

A Python TensorFlow model classifying cat and dog images using deep learning techniques for accurate and efficient image recognition.

• Host: GitHub
• URL: https://github.com/phenomsg/cat-dog-classifier
• Owner: PhenomSG
• Created: 2023-07-27T12:54:03.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-07-27T13:37:21.000Z (over 1 year ago)
• Last Synced: 2024-08-06T08:11:02.352Z (3 months ago)
• Topics: binaryclassification, jupyter-notebook, opencv, python3, tensorflow2
• Language: Jupyter Notebook
• Homepage:
• Size: 3.84 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Image Classification

## Cat and Dog Classifier

The cat and dog classifier I developed is a powerful computer vision model that utilizes the combined capabilities of OpenCV and TensorFlow for accurate image classification. Through deep learning techniques, the model has been trained on a vast dataset of cat and dog images, enabling it to distinguish between the two with impressive accuracy.

To utilize the classifier, users can input an image containing either a cat or a dog. The model processes the image, extracting relevant features, and then employs its trained knowledge to predict whether the input image contains a cat or a dog. This user-friendly approach allows for straightforward integration into various applications.

## Applications

The classifier has numerous practical applications, such as assisting in organizing large collections of pet images, automating pet identification in animal shelters or veterinary clinics, or even creating entertaining applications that sort images based on the presence of cats or dogs. Its versatility and accuracy make it a valuable tool for any task involving the classification of cat and dog images.