Cat Dog Classification
The Dogs vs Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or a cat.
Although the problem sounds simple, it was only effectively addressed in the last few years using deep learning convolutional neural networks. While the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch.
In this tutorial, you will discover how to develop a convolutional neural network to classify photos of dogs and cats.
After completing this tutorial, you will know:
- How to load and prepare photos of dogs and cats for modeling.
- How to develop a convolutional neural network for photo classification from scratch and How to improve model performance.
- How to test a photo classification model.
- How to deploy a model with the help of python framework (Flask).
About Dataset
Working of Cat vs Dog Classifier
Source code and how to use:
1. Download the dataset : Cat Dog Dataset
2. Go to my github and download code : Cat Dog Classifier
Cat-Dog-Classifier is a Deep Learning model with a good accuracy.
Cat_Dog_classifier.h5 is a trained model but if you want to train it again. So please run Cat_Dog_Classifier.ipynb in jupiter notebook or Google colab with proper training and validation dataset and then save the model. Then after training of the model, open command prompt and run Image_classification.py then you will get a url then copy the url and paste url in your browser. then your program will be run and now you can test the program with different cat or dog images.
3. After download, Extract the folder.
4. Go into the folder.
5. Open the command prompt and go to the project folder with cd command.
6. Write (python Image_classification.py) in your prompt.
7. You get a link like (http://127.0.0.1:5000/).
8. Paste in the Chrome or any browser.
9. Now you can use the model.