I guess now it should be fine. Files · segmentation · Daniil / lh-radiology-vr-models · GitLab
Add .DS_Store
to your .gitignore/
please
You use use https://gitignore.io to generate one that’s common for your stacks.
My work for the past week:
- Experimented with re-distorting bounding boxes. It looks ok
- Did a little bit of refactoring in segmentation branch
- run training for object detection (but run out of GPU quota)
Tasks for next week:
- fix MR if needed
- Prepare the detection model
My expectations / what help I expect to receive:
- Feedback on MRs
My work for the past week:
- Developed the surgical tools detection model
- Have written inference for this model
Tasks for next week:
- Create MR with detection model
- Start working towards classification / improvement of the other modules
My work for the past week:
- Made MR with detection inference feat inference: add detection inference (!2) · Merge requests · LibreHealth / LibreHealth Radiology / lh-radiology-vr-models · GitLab
- Start experimenting with classification task
Tasks for next week:
- Write inference for tools classification
- Start scene classification
My work for the past week:
- Did a MR: Classification (!3) · Merge requests · LibreHealth / LibreHealth Radiology / lh-radiology-vr-models · GitLab
- Start building a model for scene classification (the data loader works too slow, I am trying to find out a way to speed it up)
Tasks for the next week:
- Finish experiments with scene classification
- Prepare inference for it
- Prepare inference for video stream (running all steps for detection and segmentation for a video) @sunbiz correct me if this part is not detailed enough
- Complete notebooks with Detection inference results
Hi @DaniilOr I won’t be able to attend todays meeting. Please provide update on the pipeline I asked you to create with some output (maybe you can include images in reply to this thread.)
My work for the past week:
- Prepared the video annotator pipeline: Files · annotator · Daniil / lh-radiology-vr-models · GitLab
- Added examples of detection: experiments · detection · Daniil / lh-radiology-vr-models · GitLab Some of the examples can be shown here, I guess. TL DR: I have experimented with different distortion parameters, the model detects the surgical tools well for almost all parameters.
Tasks for the next week:
- Do a major code refactoring (but merging my MRs is needed), write documentation
Your weekly report is due.
Done. Please sorry for a small delay, I was in a train
My work for the past week:
- Local refactoring
- Optimisation of video processing pipeline
Tasks for the next week:
- Make the segmentation results easier to understand (because of grayscale the classes on the output are hard to identify)
- Prepare the detection demo (and upload here the video)
- Improve the speed of pipeline
- Write a comprehensive guide for running the pipeline and reusing the code
@pri2si17 correct me if something is missing or wrong here
Here are some examples of videos: DropMeFiles – free one-click file sharing service Now there is no need to move between frames using the keyboard, processing is faster and the segmentation results are easy to read. I hope this quality is fine
No issues – I just remind you – me needing to remind you isn’t going to go against you…Now, if I’m reminding you on Sundays, that’s an issue; otherwise, you’re fine.
My work for the past week:
- Major global refactoring
- Improving the processing speed
- Adding examples for detection
Tasks for the next week:
- Change the segmentation video processor so that only the surgical tool is segmented
@pri2si17 can you review Final submission (!5) · Merge requests · LibreHealth / LibreHealth Radiology / lh-radiology-vr-models · GitLab and merge it, if everything is fine?
@pri2si17 I also would like to start working towards the final submission. Are the any fixes that I have to add before moving to this part? Also, can you review the final submission post when it is ready?
Hi @DaniilOr your large MR is having merge conflicts. Can you please rebase and fix it? And thanks for the good work!