First instance segmentation training and prediction with lightning flash

Making an annotated Dataset:

125 grey-scaled images of overlapping pairs of chromosomes were annotated with makesense.ai. Annotation was saved in a single json file in COCO format:

Each image has two instances of a single "chromosome" label.

Loading and training a maskrcnn model

Lightning-flash was used to load and train a mask-rcnn model over the dataset:

Possible issue with the dataset.

The dataset has two instances of the same kind labeled as chromosome. The prediction yields only one mask:

Installation of lightning-flash

Having anaconda installed on a ubuntu 20.04 box:

Create a virtual environment, specifying the disk:

conda create --prefix /mnt/stockage/Developp/EnvPLFlash

and activate the env with:

conda activate /mnt/stockage/Developp/EnvPLFlash

Then install the libs  starting with pytorch with cuda support:

To have pytorch 1.8 with cuda support:

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts

 then 

pip install icedata

pip install lightning-flash

pip install notebook

pip install voila

Without forgetting to install lightning-flash[image] to get the instance segmentation algorithms

pip install 'icevision' 'lightning-flash[image]'

The installation can be checked running the following notebook: