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ä»å㯠1. Caffeã§MNISTãå¦ç¿ 2. å¦ç¿ä¸ã«ç²¾åº¦ãä¸ãã£ã¦ããæ§åããããã ãããã¨ãç®æ¨ã«ããã å®é¨ã§ä½¿ãç°å¢ãªã©ã«ã¤ãã¦ã¯ãä¸è¨ããåç §ãã ããã ãã¡ã¢æ¸ããUbuntu 14.04 CUDA6.5 GTX970 Caffeã¤ã³ã¹ãã¼ã« - ä¸ä¸¸åã®ã³ãã¯ã·ã§ãã¹ã MNISTã®å¦ç¿ 以éã¯MNISTãå¦ç¿ããéã®æé ã示ãã é²ãæ¹ã¯Caffe | LeNet MNIST Tutorialãåèã«ããã MNIST(http://yann.lecun.com/exdb/mnist/)ã¯LeCunããä½æããææ¸ãæåãã¼ã¿ã»ããã§ãæ§ã ãªäººéãæ¸ããï¼ï½ï¼ï¼ã®æ°åãã³ã³ãã¥ã¼ã¿ã«åé¡ãããã¿ã¹ã¯ã¨ãã¦åãçµã¾ãã¦ããã ä¾ãã°ï¼ãæ¸ãããç»åãä¸è¦§ã«ããã¨ã以ä¸ã®ããã«ãªãã ä¸äººä¸äººãæ¸ãæåã®è¦ãç®ã¯å¤æ§ãªãããå¤æ§ããå¸åãããããªã¢ã«ã´ãªãºã ãå¿ è¦ãªã¿ã¹ã¯
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Caffenetã®å¦ç¿æ¸ã¿ã¢ãã«ï¼bvlc_reference_caffenet.caffemodelï¼ããFlickrã®ç»åã使ã£ã¦fine-tuningãã¦ã¿ããåºæ¬çã«ã¯ãcaffeã®ãã¥ã¼ããªã¢ã«ãåèã«ããã ã¾ãã¯ãFlickrããç»åããã¦ã³ãã¼ãã > python examples/finetune_flickr_style/assemble_data.py --workers=1 --images=2000 --seed 831486 Downloading 2000 images with 1 workers... Writing train/val for 2000 successfully downloaded images. ãworkers=-1ãã ã¨ã¨ã©ã¼ãåºãã®ã§ããworkers=1ãã«è¨å®ãã¦å®è¡ããã 次ã«ãmodels/models/finetun
ãã¼ã¿ãã¼ã¹ã¨meanãã¡ã¤ã«ã®ä½æ â $ sh caffe_script/mean_script.sh 101_ObjectCategories train > caltech/fine_tuning/train.txt $ sh caffe_script/mean_script.sh 101_ObjectCategories test > caltech/fine_tuning/val.txt è¨äºã®ã³ãã³ãã©ã¤ã³å¼æ°ã¨éãã®ã§æ³¨æ $ build/tools/convert_imageset convert_imageset: Convert a set of images to the leveldb/lmdb format used as input for Caffe. Usage: convert_imageset [FLAGS] ROOTFOLDER/ LISTFILE
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