{"id":19010521,"url":"https://github.com/smsraj2001/cat-dog-image-classification","last_synced_at":"2026-05-15T18:02:00.266Z","repository":{"id":141637240,"uuid":"583030047","full_name":"smsraj2001/CAT-DOG-IMAGE-CLASSIFICATION","owner":"smsraj2001","description":"A Machine learning project on Cat v/s Dog image classification using CNN, VGG-16 and VGG-19 in Python","archived":false,"fork":false,"pushed_at":"2022-12-31T17:54:31.000Z","size":53110,"stargazers_count":1,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-21T15:33:39.346Z","etag":null,"topics":["cnn-classification","deep-learning","google-colab","image-classification","image-processing","kaggle-dataset","keras-tensorflow","machine-learning","matplotlib","numpy-library","python310","recognizes-images","tensorflow2","test-train-split","vgg","vgg16","vgg19"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CAT-DOG-IMAGE-CLASSIFICATION\nA Machine learning (Deep Learning) project on ```Cat v/s Dog image classification``` using ```CNN```, ```VGG-16``` and ```VGG-19``` in Python.\n- ```Link to the dataset``` : https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\n- The Dataset consists of 12500 images of Cat and 12500 images of Dog.\n\n## IMPORTANT NOTE\n- It is advised to run the notebook in ```Google colab```, as colab provides ```GPU``` which lessens the training time of the algorithm.\n- Also colab is inbuilt with all of the required python pip packages, thereby saving your time to install these packages on your local system.\n- To run the notebook in your native pc, just change the paths of image folders accordingly.\n- To use the ```TestImages```, upload the test images folders to your drive and give the path accordingly.\n- A brief description of all the 3 algorithms namely ```CNN```, ```VGG-16``` and ```VGG-19``` are available in the presentations uploaded. (Both part 1 and part 2). Explanation on methods to approach the problem, result analysis of all the 3 algorithms are provided in these presentations as well.\n- All the codes are documented for deeper understanding.\n\n#### ```NOTE``` : For any queries/corrections, please feel free to mail : sutharsanraj2001@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmsraj2001%2Fcat-dog-image-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmsraj2001%2Fcat-dog-image-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmsraj2001%2Fcat-dog-image-classification/lists"}