Replication Instructions:
https://github.com/lucyellu/inkception-3d
This repository contains training and inference code based on an I3D architecture to detect ink from image stacks. It's based off of GP winning code from Youseff and Luke with slight modifications to inference specific layers. GP winning colab here
Also credit to Johannes Rudolph's segment browser who pointed out the impact that reversing the layers makes. I found that stacking the reverse with normal with specific masking can take away some of the distracting artifacts, either from segmentation or inherent non-ink features of papyrus.
The easiest way to replicate is to clone the repo and open the jupyter notebooks in a colab environment. Then simply run the cells of interest.
git clone https://github.com/lucyellu/inkception-3d.git
Otherwise- to install and run locally:
conda create --name python310 python=3.10
conda activate python310
git clone https://github.com/lucyellu/inkception-3d.git
cd inkception-3d
pip install -r requirements.txt
note the following packages need to be installed
pytorch-lightning
typed-argument-parser
segmentation_models_pytorch
albumentations
warmup_scheduler
#if you run into cudaa or other compatibility issues, try installing with no version requirements
pip install -r requirements_noversions.txt
Download the segments that you want to inference or train with. (paths).
Submission segments include: 20230702185753 20231012184423 20231005123336 20231106155351 20231022170901
Place the downloaded segment folders in the inkception-3d folder: Place inference segments in /eval_segments Place training segments in /training_segments
Make sure each {segmentid}_mask.png and {segmentid}_inklabel.png (if training) file is in its appropriate segment folder.
Place the segment data you want to inference into ./eval_segments or adjust accordingly. The inference_stride32 script runs a checkpoint model based on inception-3d. It saves progress images in a folder designated in outputs path at 2% intervals. Adjust as needed.
Arguments found within the InferenceArgumentParser class.
Download Youseff's checkpoint here
python inference_stride32.py --segment_id 20230702185753 --model_path '/content/drive/MyDrive/inkception-3d/models/valid_20230827161847_0_fr_i3depoch=7.ckpt'
Some example outputs found at Segment Browser
Adjust the CFG class in 64x64_256stride_i3d.py These are the parameters you can adjust per inference or training run.
I found the default backbone setting to be different for the inference (efficientnet0) and training files (set to resnet) so make sure you select the one you want.
python 64x64_256stride_i3d.py