Linux / arm64
The application consists of two computer vision models - the first is a YoloV4 object detection model that looks for moles from a webcam feed, and the second is an EfficientNet B0 classification model trained on the HAM10000 dataset, that classifies a mole as either: Melanoma, Benign, Nevus, or Unknown.
The models were trained by Roujia Wang (firstname.lastname@example.org)
Either a Jetson Xavier AGX device, or the Clara AGX Developer Kit running Jetpack 4.5 or later.
sudo docker pull nvcr.io/nvidia/clara-agx/agx-dermatology-reference:v1.1
sudo docker run -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY --device /dev/videox:/dev/video0 -v /home/nvidia/results:/results nvcr.io/nvidia/clara-agx/agx-dermatology-reference:v1.1
Licenses and model files are available. They can be pulled as part of the procedure described. By pulling and using the container, you accept the terms and conditions of these licenses.
For documentation, SDK, and how to get a Clara Developer Kit: https://developer.nvidia.com/clara-holoscan-sdk
Use the NVIDIA Devtalk forum for questions regarding this Release: https://forums.developer.nvidia.com/c/healthcare/clara-holoscan-sdk/