shaderobotics/detr-detection repository overview

⁠DETR-Detection-ROS2 Wrapper

This is a ROS2 wrapper for the DEtection TRansformer (DETR)⁠. 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.

From Paper: The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors.

⁠Installation Guide

⁠Using Docker Pull

1. Install Docker⁠ and ensure the Docker daemon is running in the background.
2. Run docker pull shaderobotics/detr-detection:${ROS2_DISTRO}-${MODEL_VERSION}
3. Follow the run commands in the usage section below

⁠Build Docker Image Natively

1. Install Docker⁠ and ensure the Docker daemon is running in the background.
2. Clone this repo with git pull https://github.com/open-shade/detr_detection.git
3. Enter the repo with cd detr_detection
4. To pick a specific model version, edit the ALGO_VERSION constant in /detr_det/detr_det.py
5. Build the container with docker 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.
6. Follow the run commands in the usage section below.

⁠Model Versions

• resnet-50
• resnet-101

More information about these versions can be found in the paper⁠. The versions represent the different backbones (with resnet-101 being larger)

⁠Example Docker Command

docker pull shaderobotics/detr-detection:foxy-resnet-50

⁠Usage

⁠Run the YOLOS Node

Run docker run -t --net=host shaderobotics/detr-detection:${ROS_DISTRO}-${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 docs here⁠.

⁠Parameters

This wrapper utilizes 4 optional parameters to modify the data coming out of the published topics as well as the dataset YOLOS utilizes for comparison. Most parameters can be modified during runtime. However, if you wish to use your own dataset, you must pass that parameter in before runtime. If you are unsure how to pass or update parameters before or during runtime, visit the official ROS2 docs here⁠.

The supported, optional parameters are...

You do not need to specify any parameters, unless you wish to modify the defaults.

⁠Topics

⁠Testing / Demo

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 DETR node and processes it for you to observe the outputs.

To run this, run docker build . -t --net=host [name], then docker run -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