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Describe latest analysis of lila metadata
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analysis and generation of metadata in commit 15fccf29
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Remove other non-creature images (24 unique original labels with no corresponding scientific name)
some examples are problem, blurred, vehicle, unknown, vegetation obstruction, misfire, unidentified
see lilabc_CT notebook for details
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remove entries labeled as empty
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Add the taxonomy mapping from original download
referenced in notebook in bfe306cec31dccb3b7423549a2f3db328b95bb68
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Add paired py file for notebook from first analysis
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Updated CSV downloaded from LILA for updated analysis
downloaded today
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Add note about licensing
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Create README.md
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Ignore pycache, ds_store, etc.
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Organized data and notebook to appropriate folders and fixed data calls in notebook.
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Stats on number of images per species added in.
Further info on the number species with at least 20 or 40 images and the general family distribution of these. -
Main CSV filtered to just those with species labels and only one animal per image.
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Some more data cleaning and stats, deduped images and reduced down to species designation
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LILA BC full camera trap information with notebook exploring available data.
The CSV includes links to all images for the 18 camera trap datasets plus taxonomic info. -
initial commit
