This is a ROS2 wrapper for the Residual Network, ResNet
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This is a ROS2 wrapper for the Residual Network, ResNet. We utilize huggingface and the transformers for the source of the algorithm. The main idea is for this container to act as a standalone interface and node, removing the necessity to integrate separate packages and solve numerous dependency issues. We have built all different versions of the algorithm (i.e. resnet-50, resnet-101, etc.) for each ROS2 distribution.
Model is trained on the ImageNet-1000 dataset.
docker pull shaderobotics/resnet:${ROS2_DISTRO}-${MODEL_VERSION} we support all ROS2 distributions along with all model versions found in the model version section below.git pull https://github.com/open-shade/resnet.gitcd deitALGO_VERSION constant in /resnet/resnet.pydocker build . -t [name]. This will take a while. We have also provided associated cloudbuild.sh scripts to build on GCP all of the associated versions.resnet-50resnet-101resnet-18resnet-34resnet-152More information about these versions can be found in the paper. 50, 101 represent the size of the model.
docker pull shaderobotics/resnet:foxy-resnet-50
Run docker run -t --net=host shaderobotics/resnet:${ROS_DISTRO}-${MODEL_VERSION}. Your node should be running now. Then, by running ros2 topic list, you should see all the possible pub and sub routes.
For more details explaining how to run Docker images, visit the official Docker documentation here. Also, additional information as to how ROS2 communicates between external environment or multiple docker containers, visit the official ROS2 (foxy) docs here.
| Name | IO | Type | Use |
|---|---|---|---|
| resnet/image_raw | sub | sensor_msgs.msg.Image | Takes the raw camera output to be processed |
| resnet/result | pub | String | Outputs the classification label from ImageNet 1000 Classes as a string |
To test and ensure that this package is properly installed, replace the Dockerfile in the root of this repo with what exists in the demo folder. Installed in the demo image contains a camera stream emulator by klintan which directly pubs images to the ResNet node and processes it for you to observe the outputs.
To run this, run docker build . -t [name], then docker run --net=host -t [name]. Observing the logs for this will show you what is occuring within the container. If you wish to enter the running container and preform other activities, run docker ps, find the id of the running container, then run docker exec -it [containerId] /bin/bash
Content type
Image
Digest
sha256:303c6a0d5…
Size
3.7 GB
Last updated
about 4 years ago
docker pull shaderobotics/resnet:rolling-tiny