A naive U-Net baseline and submission demo for the microscopy image segmentation challenge in NeurIPS 2022
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Install requirements by
python -m pip install -r requirements.txtDownload training data to the data folder
Preprocess dataset with
python data/pre_process_3class.pyWe convert the instance label in ground truth to a three-class label (0-background, 1-interior, 2-edge). After the segmentation, we can convert the three-class label map to an instance map by
skimage.measure.label.Please always keep in mind that this is a instance segmentation task. The baseline is a very simple and naive solution. We highly recommend trying the state-of-the-art methods that mentioned in the end.
See all training options with
python baseline/model_training_3class.py --helpTrain baseline model with
python baseline/model_training_3class.py --data_path 'path to training data' --batch_size 8Run
python predict.py -i input_path -o output_pathYour prediction file should have at least the two arguments:
input_pathandoutput_path. The two arguments are important to establishing connections between local folders and docker folders.
Run
python compute_metric.py --gt_path path_to_labels --seg_path path_to_segmentationCells on the boundaries are not considered during evaluation.
We recommend this great tutorial: https://nbviewer.org/github/ericspod/ContainersForCollaboration/blob/master/ContainersForCollaboration.ipynb
The docker is built based on MONAI
docker pull projectmonai/monaiPrepare Dockerfile
FROM projectmonai/monai:latest
WORKDIR /workspace
COPY ./ /workspacePut the inference command in the predict.sh
# !/bin/bash -e
python predict.py -i "/workspace/inputs/" -o "/workspace/outputs/"The
input_pathandoutput_pathaugments should specify the corresponding docker workspace folders rather than local folders, because we will map the local folders to the docker workspace folders when running the docker container.
The submitted docker will be evaluated by the following command:
docker container run --gpus "device=0" -m 28G --name teamname --rm -v $PWD/CellSeg_Test/:/workspace/inputs/ -v $PWD/teamname_seg/:/workspace/outputs/ teamname:latest /bin/bash -c "sh predict.sh"--gpus: specify the available GPU during inference-m: spedify the maximum RAM--name: container name during running--rm: remove the container after running-v $PWD/CellSeg_Test/:/workspace/inputs/: map local image data folder to Docker workspace/inputs folder.-v $PWD/teamname_seg/:/workspace/outputs/: map Docker workspace/outputs folder to local folder. The segmentation results will be in $PWD/teamname_outputsteamname:latest: docker image name (should be teamname) and its version tag. The version tag should be latest. Please do not use v0, v1... as the version tag/bin/bash -c "sh predict.sh": start the prediction command. It will load testing images from workspace/inputs and save the segmentation results to workspace/outputsAssuming the team name is baseline, the Docker build command is
docker build -t baseline . Test the docker to make sure it works. There should be segmentation results in the baseline_seg folder.
docker container run --gpus "device=0" -m 28G --name baseline --rm -v $PWD/TuningSet/:/workspace/inputs/ -v $PWD/baseline_seg/:/workspace/outputs/ baseline:latest /bin/bash -c "sh predict.sh"During the inference, please monitor the GPU memory consumption using
watch nvidia-smi. The GPU memory consumption should be less than 10G. Otherwise, it will run into an OOM error on the official evaluation server.
docker save baseline | gzip -c > baseline.tar.gzUpload the docker to Google drive (example) or Baidu net disk ([example]()) and send the download link to NeurIPS.CellSeg@gmail.com.
Please do not upload the Docker to dockerhub!
The naive baseline's primary aim is to give participants out-of-the-box scripts that can generate successful submisions. Thus, there are many ways to surpass this baseline: