Sids Status Report for April 23rd

What did you personally accomplish this week on the project?

The majority of my work this week involved finishing the process of training the detection model properly and ensuring that this was done properly.

I faced many problems with this process; first, our setup on Colab did not work in time either due to high latency during the scanning of the dataset files from google drive. Following this, I decided to use my teammate’s old desktop for training the model since it had a GPU. After managing to transfer all the files to the desktop using the public sharing URL provided by google drive.

I set up the environment needed for training the neural net on the desktop and this took much longer than I had expected. We ran into many problems and with my team’s help, I was able to modify the Python and CUDA versions on the desktop to be compatible with each other and YOLOv5. We also had to make changes to the CUDA and PyTorch versions that were running on the JESON Nano based on the changes we made.

While the model was training, we worked on the search algorithm for the camera on the JETSON. Once the model was trained we got statistics regarding the recall and accuracy which we were happy with.

Recall ~ 93%

Acuraccy ~ 92.8%

After adding code to the scripts on the JETSON, the detection model can be integrated into the algorithm. We move the trained best weights and call the predict function for every frame the camera sees until the detection model tells us to transfer control to track.

We planned out the testing process and I picked images we are going to print to test the project in the coming days.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

The progress is slightly behind due to all the problems faced while training the detection model and the fact that errors would show up hours into running the training script.

To make up for this, my team helped me with implementing a basic simple search algorithm with the camera for now and we plan to speed up our plans for testing which would put us back on track.

What deliverables do you hope to complete in the next week?

Test results for the detection and integrated system.

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