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        "author": "Sergiusz Bleja",
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        "description": "# duckgoose\nUtility scripts for the online [fast.ai](www.fast.ai) course. There are two main parts: one to download and organise arbitrary image classes, and one to highlight what parts of an image is activating the decision for a classification.\n\n1. Utility for Lesson 1 experimentation with external image classes. The script:\n* Downloads images from google images for specific classes\n* Sanity checks that images can be opened and have three channels\n* Organises the images into separate folders (train/valid/test + classes) as expected by the fast.ai library\n\n2. Utility for creating Class Activation Maps for both classifications.\n\n## Prerequisites \n\n* `chromedriver` is required. On ubuntu/debian: `sudo apt-get chromium-chromedriver`\n\n## Installation\n\n```python\npip install duckgoose\n```\n\n## Usage\n\n### Fetching, sanity checking and organising images\n\n```python\nfrom duckgoose import fetchImagesAndPrepForClassification\n\n# dictionary structure `class_name => search term`\nimage_classes = { 'ducks' : 'ducks -rubber' , 'geese' : 'geese' }\ndownload_path = '/home/myuser/data/downloaded_from_google'\noutput_path = '/home/myuser/data/ducksgeese/'\nnumber_of_images = 100\n\nfetchImagesAndPrepForClassification(image_classes, download_path, output_path, number_of_images)\n```\n\n### Create Class Activation Maps (CAM)\n*Note*: This was implemented for fastai v2 part 1.\nHere is a full example of creating a class activation maps for ducks and geese using fast ai. \n\n```python\nfrom fastai.imports import *\nfrom fastai.transforms import *\nfrom fastai.conv_learner import *\nfrom fastai.model import *\nfrom fastai.dataset import *\nfrom fastai.sgdr import *\nfrom fastai.plots import *\n\nfrom duckgoose.cam import calculateAndChartHeatZoneFor\n\nPATH = \"data/ducksgeese/\"\nsz=224\narch = resnet34\nbs = 64\n\nm = arch(True)\nm = nn.Sequential(*children(m)[:-2], \n                  nn.Conv2d(512, 2, 3, padding=1), \n                  nn.AdaptiveAvgPool2d(1),\n                  Flatten(), \n                  nn.LogSoftmax())\n\ntfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\ndata = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs)\nlearn = ConvLearner.from_model_data(m, data)\n\nlearn.freeze_to(-4)\n\n_, val_tfms = tfms_from_model(learn.model, sz)\n\nlearn.fit(0.01, 2)\n\ncalculateAndChartHeatZoneFor('./data/ducksgeese/test/ducks/ducks_427.jpg', val_tfms, learn)\n```\n\n![Duck and goose heatmap](images/duck.png)\n\n\n# License\n[The MIT License (MIT)](LICENSE.txt)",
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