Sure thing. The example gets buried in the site-packages folder I guess. But anyways, the format is super simple:
first column is your search term, second column is a list of expressions you don’t want to appear in the final class names (that are based on the search term)…
Let me give you a quick example:
guitar gibson les paul,guitar
guitar "g&l" legacy, guitar
This will create two classes and exclude “guitar” int he final class names (they will be: gibson_les_paul, gandl_legacy). I added exclude terms since they help you find better results in the actual query on google/ bing/ etc.
Wow, looks promising, I Just looked at it briefly and like the fact that there is option to clean the download images quickly, will try it in detail, in next few days and will post feedback here. In the meanwhile, Great work @cwerner.
-m is a great addition to the function. Specifying 50 images sped it up significantly. Using -m 50, -c GOOGLE and four search queries, the process took 43 seconds. Using all three services took just over 2.5 minutes.
I used pip show fastclass to find the install location y’day, to no avail. In the fastclass folder, I see deduplicate.py, fc_clean.py, fc_download.py, imageprocessing.py, misc.py, __init__.py and __pycache__/. In the …dist-info folder I see entry_points.txt, INSTALLER, LICENSE, METADATA, RECORD, WHEEL and top_level.txt.
Ah, I mean multiple labels per image (satellite images). I haven’t been able to find a way to evaluate the trained model. The notebook suggests uploading to Kaggle for that specific example, but I’d like to use the other “interp” methods available in the single class classification. Do you have any suggestions?