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How to use this repository: if you know exactly what you are looking for (e.g. you have the paper name) you can Control+F to search for it in this page (or search in the raw markdown). Land use classification dataset with 21 classes and 100 RGB TIFF images for each class. Each image measures 256x256 ...
Instructions. Configure hatch to create virtual env in project folder. After, create all the python environments needed by running hatch -e all run tests. Finally, configure vscode to use one of the selected environments: cmd + shift + p -> python: Select Interpreter -> Pick one of the folders in ./.env.
The Kaggle API follows the Data Package specification for specifying metadata when creating new Datasets and Dataset versions. Next to your files, you have to put a special dataset-metadata.json file in your upload folder alongside the files for each new Dataset (version). Here's a basic example for dataset-metadata.json:
What worked for me was creating a token without expiring the old one(s). Based on the toast notifications in the Kaggle web app, it looked like clicking "Expire API Token" followed by "Create New API Token" actually resulted in the new token getting created and then immediately being expired.
To run integration tests on your local machine, you need to set up your Kaggle API credentials. You can do this in one of these two ways described this doc. Refer to the sections: Using environment variables. Using credentials file. After setting up your credentials by any of these methods, you can run the integration tests as follows:
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Step 1: Setup Kaggle CLI. The FraudDatasetBenchmark object is going to load datasets from the source (which in most of the cases is Kaggle), and then it will modify/standardize on the fly, and provide train-test splits. So, the first step is to setup Kaggle CLI in the machine being used to run Python.
Various datasets provided by Kaggle (Explore, analyze, and share quality data. Learn more about data types ...