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  "source_url": "https://www.kaggle.com/competitions/dogs-vs-cats-mvml-2020/data",
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      "name": "rules",
      "content": "# One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n# No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n# Team Mergers\nTeam mergers are not allowed in this competition.\n\n# Team Limits\nThe maximum size of a team is 1 participant.\n\n# Submission Limits\n- You may submit a maximum of 5 entries per day.\n- You may select up to 2 final submissions for judging.",
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      "content": "train on cats_vs dogs predict class labels (dog = 1, cat = 0) for test",
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      "content": "In this competition, you'll write an algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F459781%2F2563921e3dcfdbce26ddbf7b581cddfa%2Fwoof_meow.jpg?generation=1588719558906997&alt=media)\n\n# The Asirra data set\n\nWeb services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.\n\nAsirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:\n\nAsirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research.\n\n# Image recognition attacks\n\nWhile random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459.\n\n# State of the art\n\nThe current literature suggests machine classifiers can score above 80% accuracy on this task 1. Therfore, Asirra is no longer considered safe from attack. We have created this contest to benchmark the latest computer vision and deep learning approaches to this problem. Can you crack the CAPTCHA? Can you improve the state of the art? Can you create lasting peace between cats and dogs?\n\nOkay, we'll settle for the former.\n\n## Acknowledgements\nWe extend our thanks to Microsoft Research for providing the data for this competition.\n\n- Jeremy Elson, John R. Douceur, Jon Howell, Jared Saul, Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization, in Proceedings of 14th ACM Conference on Computer and Communications Security (CCS), Association for Computing Machinery, Inc., Oct. 2007",
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      "name": "Evaluation",
      "content": "Performance is evaluated on the percentage of correctly labelled images. To determine your odds of breaking the Asirra CAPTCHA, raise your percentage to the 12th power.\n\n\"But classification accuracy is a flawed metric!\" you scream at your monitor in fury, \"my genius requires you accept the posterior probability of my predictions!\" That may be true, but sometimes simplicity is just nice. Here there are only dogs and cats… no 0.5 dog-cat hybrid guesses allowed!\n\n# Submission Format\nYour submission should have a header. For each image in the test set, predict a label for its id (1 = dog, 0 = cat):\n\n```\nid,label\n1,0\n2,0\n3,0\netc...\n```",
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