I’ve read the first chapter and the mosquito was… beautiful. Actually lol’ed at that.
Anyway very good material. Correctly identifies lack of mistrust in the data in most engineers. Doesn’t go far enough in saying that folks like to play with toys (models) instead of doing tireless and boring but important work.
I have an ML project I started that involved manually labeling around 10,000 still frames of my hand on a guitar fretboard playing various chord shapes. I made a little web app with a keyboard interface for quickly adding labels to images. I got up to around an image a second when I got in the zone. I finished the dataset, got distracted by the birth of my son, and have literally done none of the “fun stuff” yet!
If anyone wants to have a crack at the data, it’s in git-lfs and here:
If you ever get back into it, I bet you can 10x to 20x that speed with augmented labeling.
Once you have >100 frames labeled you can put a classifier in the loop and only have to label the % of frames it gets wrong.
I usually set up a view with 10x10 samples only containing samples labeled as a single class by the classifier, then I mark those it got wrong as unlabeled and move on to the next batch. With an 80% accurate classifier you can get 80 samples labeled every 5 seconds or so.
And if you retrain the classifier regularly on the newly labeled samples you can improve its accuracy and the speed of labeling with it.
A data scientist friend of mine had some success with Figure8 but I haven't used it myself.
Honestly I always roll my own, it's dead fast to throw a simple GUI together in tkinter and it makes it easy to integrate your own models and custom sample rendering/plotting.
That is if you're doing simple discrete class labeling, as opposed to more complex labeling like box-labeling for image segmentation, or text2speech labeling for example.
Thx for sharing the link to your work. Interesting idea. A few thoughts:
1) Congrats on the birth of your child. I imagine you have zero time now but as they get older, you start getting your time back. I went through this and now I can sneak in personal projects while the kids are in their activities, late at night, early am. Be aware your body and stamina declines as you age. I am getting close to mid 40s and I can feel it.
2) The previous poster presented an interesting idea about putting a basic classifier in the loop. The challenge is how do you if the classifier gets it wrong. Confidence scores you get from the logits are extremely flakey. I think one solution is to metric learning methods (contrastive loss instead of cross entropy). I have seen some papers that dance around this but have not seen anything fully baked from a scientific perspective.
3) Your task is an interesting action recognition task. You should seriously consider putting it on kaggle or write a paper on it (and release the dataset). The easy off-the-shelf model you could try on this data for a video classification task like this is possibly X3D. But there are a variety of other methods (I'm a researcher in the field).
Anyway very good material. Correctly identifies lack of mistrust in the data in most engineers. Doesn’t go far enough in saying that folks like to play with toys (models) instead of doing tireless and boring but important work.