Commit 6464f65d authored by Nieuwenhuizen, Ard's avatar Nieuwenhuizen, Ard
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notebooks for review

parents
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"A great source of many images is kaggle, which a data science competition platform. Many people upload loads of data there, oftten together with a goal. That is, they want to find a way to get information from the data. Some of these datasets contain images. For instance of healthy/infected leaves of crops ( https://www.kaggle.com/hadjerhamaidi/date-palm-data, https://www.kaggle.com/saroz014/plant-disease#dataset.zip) or images of crops an weeds (https://www.kaggle.com/limitpointinf0/crop-vs-weeds/data) or images of fresh and rotten fruits (https://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification). The datasets change over time, so it might be that the links are outdated. A possible project is:\n",
"\n",
"\n",
"1. make a kaggle account\n",
"2. download a data set with images in different classes (for instance healthy and infected)\n",
"3. make a classifyer that can distinguish the different classes with the machine visionn tools you have learned\n",
"4. explain the choises you have meade in the process.\n"
]
}
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%% Cell type:markdown id: tags:
A great source of many images is kaggle, which a data science competition platform. Many people upload loads of data there, oftten together with a goal. That is, they want to find a way to get information from the data. Some of these datasets contain images. For instance of healthy/infected leaves of crops ( https://www.kaggle.com/hadjerhamaidi/date-palm-data, https://www.kaggle.com/saroz014/plant-disease#dataset.zip) or images of crops an weeds (https://www.kaggle.com/limitpointinf0/crop-vs-weeds/data) or images of fresh and rotten fruits (https://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification). The datasets change over time, so it might be that the links are outdated. A possible project is:
1. make a kaggle account
2. download a data set with images in different classes (for instance healthy and infected)
3. make a classifyer that can distinguish the different classes with the machine visionn tools you have learned
4. explain the choises you have meade in the process.
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