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DeployableObject Detection in Images
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By IBM Developer Staff | Published March 28, 2019
Artificial intelligenceDeep learningVisual recognitionImage ClassificationObject Detection in Images
This model detects nuclei in a microscopy image and specifies the pixels in the image that are assigned to each nucleus. The model is developed based on the architecture of Mask R-CNN using Feature Pyramid network (FPN) and a ResNet50 backbone. Given an image (of size 64 x 64, 128 x 128 or 256 x 256), this model outputs the segmentation masks and probabilities for each detected nucleus. The mask is compressed using Run-length encoding (RLE).
The model is based on the TF implementation of Mask R-CNN.
The model is trained on the Broad Bioimage Benchmark Collection (Accession number BBBC038, Version 1) dataset of annotated biological images.
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-nucleus-segmenter
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Nucleus-Segmenter/master/max-nucleus-segmenter.yaml
You can test or use this model
Once deployed, you can test the model from the command line. For example:
$ curl -F "image=@samples/example.png" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
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