Add ensembling methods for tiling to Anomalib #1727
Description
Discussed in #1131
Originally posted by blaz-r June 15, 2023
Project abstract
When detecting defects in high-resolution images, we encounter many challenges. One of those is that models don’t work well on such a large scale, and by downsampling, we would lose information. This issue can be solved by using a tiling mechanism, where we split the image into smaller parts and process those. This way we keep all the information and the models can still fit into memory.
Anomalib already has a tiling mechanism, but the problem is that models are trained on all tiles combined, which reduces the advantages of locally-aware models that require fixed position and orientation. For cases like this, an ensemble approach will be developed.
This involves splitting data to sections using an already existing tiling mechanism. Separate model will then be trained for each section. Finally, predictions will be merged in the post-processing stage. This approach will include evaluation and comparison of performance, while also taking efficiency into account, to clearly depict advantages and gain over non-ensemble methods.
The outcome of the project will be above described mechanism that works for all existing model architectures and any new ones that will be added.
Purpose of GSOC discussions thread
This project is a part of OpenVINO GSOC. GSOC is all about open source software and promoting of community collaboration on various projects. That is why this discussion thread will be used for active updates on the progress as well as for community to have insight and to provide suggestions.
So if you have any suggestions or questions, feel very welcome to put them bellow :)