Repo for my Tensorflow/Keras CV experiments. Mostly revolving around the Danbooru20xx dataset
Framework: TF/Keras 2.7
Training SQLite DB built using fire-egg’s tools: https://github.com/fire-eggs/Danbooru2019
Currently training on Danbooru2021, 512px SFW subset (sans the rating:q images that had been included in the 2022-01-21 release of the dataset)
Anonymous, The Danbooru Community, & Gwern Branwen; “Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset”, 2022-01-21. Web. Accessed 2022-01-28 https://www.gwern.net/Danbooru2021
06/02/2022: great news crew! TRC allowed me to use a bunch of TPUs!
To make better use of this amount of compute I had to overhaul a number of components, so a bunch of things are likely to have fallen to bitrot in the process.
I can only guarantee NFNet can work pretty much as before with the right arguments.
NFResNet changes should have left it retrocompatible with the previous version.
ResNet has been streamlined to be mostly in line with the Bag-of-Tricks paper (arXiv:1812.01187) with the exception of the stem. It is not compatible with the previous version of the code.
The training labels have been included in the 2021_0000_0899 folder for convenience.
The list of files used for training is going to be uploaded as a GitHub Release.
Now for some numbers:
compared to my previous best run, the one that resulted in NFNetL1V1-100-0.57141:
- I’m using 1.86x the amount of images: 2.8M vs 1.5M
- I’m training bigger models: 61M vs 45M params
- … in less time: 232 vs 700 hours of processor time
- don’t get me started on actual wall clock time
- with a few amenities thrown in: ECA for channel attention, SiLU activation
And it’s all thanks to the folks at TRC, so shout out to them!
I currently have a few runs in progress across a couple of dimensions:
- effect of model size with NFNet L0/L1/L2, with SiLU and ECA for all three of them
- effect of activation function with NFNet L0, with SiLU/HSwish/ReLU, no ECA
Once the experiments are over, the plan is to select the network definitions that lay on the Pareto curve between throughput and F1 score and release the trained weights.
One last thing.
I’d like to call your attention to the tools/cleanlab_stuff.py script.
It reads two files: one with the binarized labels from the database, the other with the predicted probabilities.
It then uses the cleanlab package to estimate whether if an image in a set could be missing a given label. At the end it stores its conclusions in a json file.
This file could, potentially, be used in some tool to assist human intervention to add the missing tags.