frustration is the word that describes my participation in this challenge. I spent around 1h to build the actual model but days to make a successful submission. Participation should definitely be more straightforward. A must is to enable “debug” with instant error logs. Besides
submitting the same code sometimes failed and sometimes went through.
sometimes I would get timeout error after 10h and sometimes not
it doesn’t make sense to me why the evaluation is so slow when we should have <40 000 images
it also doesn’t make sense to me to organize a challenge and then remain silent
@shivam Hi, thanks for that. Git lfs is working for me but what is git lfs migrate for ? Where must I store the real model ? Is there something to adapt in the submission for that ?
Hi git lfs migrate is for transferring any older commit to start using lfs. This is useful in case you have lots of older commit (intended/unintended) and want those files to migrate to LFS based in future.
@amapic Your master branch contains the aicrowd-api but you submission branch does not. The environment.yml file in submission-v0.22 does not contain the api.
as @ValAn told you, it’s better if you don’t change the defaults. But if you still need to change, make sure to just change the second parameter from the call to os.getenv.
This is because when you submit your code aicrowd expects you to “read” those paths from environment variables they’ve set.
For you to test that it works on your local machine it should be enough with the default values and uncompressing both test_metadata_small.tar.gz and test_images_small.tar.gz in the data folder. You can download both of those files in the resources page
Personally what I do is simply generate a “fake” random image, but I guess there are better ways (more efficient / scoring higher) my pseudocode would be like:
try :
image = read(file)
except :
image = random
Final tip: Be sure to add a line with a corrupt / non-existent image file to the test_metadata_small.csv mentioned earlier, so you can also be sure your code can handle errors when reading the images.