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        "description": "<p align=\"center\">\n  <img width=\"auto\" height=\"auto\" src=\"https://github.com/koji/icTrainer/blob/master/top.png\" style=\"border: none;\noutline: none;\">\n</p>\n\n[![PyPI version](https://badge.fury.io/py/pypi.svg)](https://pypi.org/project/ictrainer/)\n### icTrainer is a python module which allows users to train image classifier easily\n\nBasically, this module is for `python3`\n## Install\n\n```\n$ pip install ictrainer\n```\nAlso you can install manually.  \n```\nclone repo\n$ git https://github.com/koji/icTrainer.git\n$ cd icTrainer/ictrainer\n$ python setup.py install\n```\n\n## How to Use\nIn this gude, we will create a dog/cat image classifier.\n### 1.Collect Images\nhttps://icrawler.readthedocs.io/en/latest/   \n\n```\n$ ictrainer --mode collect --keyword dog -n 250\n$ ictrainer --mode collect --keyword cat -n 250\n```\nYou'll have dogs & cats images under `dataset` folder.\n\n\n### 2. Resize images\nIn this step, we will change all images size for training. The current input size must be `320 x 180`(required).\nThis step may be mess up images you collected, so you need to check all images manually. In the furture, there will be a function that save your time.  \n\n```\n$ ictrainer --mode resize --target dog\n$ ictrainer --mode resize --target cat\n```\n\nFor people want to use resize mode for other thing, you can use reize images with the following command.  \nThe folder structure should be the same the above.  \n```\n$ ictrainer --mode resize --target cat --image_width 480 --image_height 320\n```\n\n### 3.Create folders for classes\nThis step, we'll need to create folders and distribute images to `train` & `validation` folder.\n\n#### 3-1. create folders\nCreate a couple of folders under dataset.   \nThis step will be automated in the future.  \n```\n dataset\n    \u251c\u2500\u2500 train\n    \u2502\u00a0\u00a0 \u251c\u2500\u2500 cat\n    \u2502\u00a0\u00a0 \u2514\u2500\u2500 dog\n    \u2514\u2500\u2500 val\n        \u251c\u2500\u2500 cat\n        \u2514\u2500\u2500 dog\n```\n\n#### 3-2. distribute images\nMove images we got via `image collect mode`. In this case, probably we have 250 images for each other.\nWe will put 225 images for train and 25 images for validation so that `train/dog` has 225 images and `validation/dog` has 25 images. The cats should be the same.\n\n### 4.Train Images\nThere are some options we need to put. The most important one is `--classes` which will be labels. In this case, we have dog & cat, so we need to put them as classes.\n`--batch`: batch size default 16        \n`--epoch`: epoch default 30       \n`--mname`: output model name      \n`--lr`: learning rate default 1e-3       \n`momentum`: mementum default 0.9     \n\nWe will use default settings.\n\n```\n$ ictrainer --mode train --classes \"cat\" \"dog\" --mname \"dogAndcat_\"\n```\n\n## code\nThis code will be pushed soon. (cleaning up now)\n\n## pre-train model\n#### `smart device`   \nhttps://github.com/koji/icTrainer/blob/master/model/smartdevice_epoch30.h5      \n```\nclasses = ['echo', 'echoplus', 'echoshow', 'googlehome', 'googlehomemini', 'nest']   \n```",
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