This week I worked on the final integration of the walk sign image classification model into the board and docker environment. We first further optimized the walk sign model. I trained it on two different datasets: one that included a lot of non-crosswalk images labeled as “don’t walk”, and one that only included crosswalk images. I also implemented the code logic for transitioning from walk sign image classification to cross walk object detection. Initially, the model worked fine on the board, but uploading the other object detection model, we realized there were Python dependency conflicts. This is still currently an issue, as there have been challenges in trying to convert the tensorflow model to a pytorch model. One attempt was to change the walk sign model from a tensorflow model to a pytorch model. This involved first saving the model in the .tf format, and then converting it to .onnx using (python -m tf2onnx.convert –saved-model walksignmodel –output walksignmodel.onnx), and then converting that to .pt. However, this has had many other python dependency issues with the onnx libraries too. My plan for this weekend is to resolve this issue as soon as possible.
Update on 4/20/2025: The pytorch issue has been resolved, see team status report for update.