【推荐】迁移学习：用0.046%的训练样本(6张图片)超过2013 Kaggle猫狗识别竞赛领先水平（附代码）

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点击上方 “机器学习研究会” 可以订阅 摘要   转自：爱可可_爱生活 In 2013 Kaggle ran the very popular dogs vs cats competition. The objective was to train an algorithm to be able to detect whether an image contains a cat or a dog. At that time, as stated on the competition website, the state of the art algorithm was able to tell a cat from a dog with an accuracy of 82.7% after having been trained on 13 000 cat and dog images. My results I applied transfer learning which is a technique where you take a model trained to carry out some other though similar task and you retrain it to do well on the task at hand. I fine tuned a VGG19 model on a total of 6 randomly selected images (you can find the pictures of our protagonists below). I achieved an accuracy of 89.97% after 41 epochs of training. The validation set size was 24 994. Being a fan of repro ………………………………