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This video tutorial explains the process of fine tuning Detectron2 for instance segmentation using custom data. It walks you through the entire process, from annotating your data, to training a model, to segmenting images, to measuring object morphological parameters, to exporting individual masks (results) as images for further processing.
Code generated in the video can be downloaded from here: github.com/bnsreenu/python_fo...
All other code:
github.com/bnsreenu/python_fo...
Detectron2 repo: github.com/facebookresearch/d...
Annotations were done using Makesense: www.makesense.ai/
Dataset from: leapmanlab.github.io/dense-cell/
Direct link to the dataset: www.dropbox.com/s/68yclbraqq1...
Data courtesy of:
Guay, M.D., Emam, Z.A.S., Anderson, A.B. et al.
Dense cellular segmentation for EM using 2D-3D neural network ensembles. Sci Rep 11, 2561 (2021).
Data annotated for 4 classes:
1: Cell
2: Mitochondria
3: Alpha granule
4: Canalicular vessel