Preliminaries

In the weekly meeting held on 06 June, we discussed the previous work and outlined the project's current focus, which is to extend the capabilities of the RoboticsAcademy platform by integrating deep learning models in ONNX format into both existing and new exercises. The goal is to enable users to upload their own models and run inference within the RADI (RoboticsAcademy Docker Image) environment. Initial work includes exploring the architecture of the RoboticsAcademy Docker image, creating Human Detection and Digit Classification exercises, and integrating a file upload mechanism for ONNX models. This week also involves studying PyTorch to better understand deep learning workflows and ensure compatibility during ONNX model conversion and deployment.

Objectives

  • Convert semantic segmentation from labels to RGB images
  • Create Human Detection and Digit Classification exercises
  • Implement support for user-uploaded ONNX models
  • Beta test the exercises to ensure functionality and usability
  • Update local exercise database
  • Study PyTorch and ONNX

Bug Fixing

In production mode, I discovered that the education link in the Computer Vision exercise was incorrect. This issue caused navigation errors for users attempting to access related resources or documentation. I have identified the source of the incorrect link and am currently working on updating it to ensure proper functionality in the production environment.

Issue

issue #3106

Pull Request

PRs #3113

Execution

I created two branches, one for human detection and another for the digit classification exercise. This branch that contains both exercises. I also updated the local database.

week-2-img-1

Human Detection and Digit Classification are available in Prototype mode.

Results

Demonstrate human detection and digit classification exercises with DL model.

References

[1] Human Detection Exercise using Deep Learning

[2] Digit Classification Exercise using Deep Learning