![\"Woof](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3362/media/woof_meow.jpg\")
The train folder contains 25,000 images of dogs and cats. Each image in this folder has the label as part of the filename. The test folder contains 12,500 images, named according to a numeric id. For each image in the test set, you should predict a probability that the image is a dog (1 = dog, 0 = cat).
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "In 2013, we hosted one of our favorite for-fun competitions: Dogs vs. Cats. Much has since changed in the machine learning landscape, particularly in deep learning and image analysis. Back then, a tensor flow was the diffusion of the creamer in a bored mathematician's cup of coffee. Now, even the cucumber farmers are neural netting their way to a bounty.
\nMuch has changed at Kaggle as well. Our online coding environment Kernels didn't exist in 2013, and so it was that we approached sharing by scratching primitive glpyhs on cave walls with sticks and sharp objects. No more. Now, Kernels have taken over as the way to share code on Kaggle. IPython is out and Jupyter Notebook is in. We even have TensorFlow. What more could a data scientist ask for? But seriously, what more? Pull requests welcome.
\n
We are excited to bring back the infamous Dogs vs. Cats classification problem as a playground competition with kernels enabled. Although modern techniques may make light of this once-difficult problem, it is through practice of new techniques on old datasets that we will make light of machine learning's future challenges.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Submissions are scored on the log loss:
\r\n$$
\\textrm{LogLoss} = - \\frac{1}{n} \\sum_{i=1}^n \\left[ y_i \\log(\\hat{y}_i) + (1 - y_i) \\log(1 - \\hat{y}_i)\\right],
$$
where
\r\nA smaller log loss is better.
\r\nFor each image in the test set, you must submit a probability that image is a dog. The file should have a header and be in the following format:
\r\nid,label", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n
1,0.5
2,0.5
3,0.5
...
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 5 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 9/2/2016 9:25 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 3/2/2017 11:59 PM UTC
\r\n\t\r\n