Rebeca Villarraso

Computer Vision Testing Analyst

1 minute read

Objectives

  1. Continue adapting DeepLabV3+ with the RELLIS-3D and RUGD datasets.
  2. GitHub Pages.
  3. Perform Semantic Segmentation on Cityscapes dataset.
  4. Study the metrics and models proposed on the Cityscapes dataset website: https://www.cityscapes-dataset.com/benchmarks/

Progress This Week

1. DeepLabV3+ on Rellis-3D

This week I have had different problems. I have not been able to adapt the DeepLabV3 model with the RELLIS-3D dataset, it requires image preprocessing that has not been applied. On the other hand, I have not been able to successfully create the Github pages sections.

2. Github Pages

During today’s meeting I met Sergio who will help me in the development of the TFM. He was telling me how to manage Github pages to clone the “docs” folder and be able to make weekly posts.

3. Cityscapes Semantic segmentation

Performed semantic segmentation with DeepLabV3 on Cityscapes dataset.

See Notebook: https://github.com/RoboticsLabURJC/2024-tfm-rebeca-villaraso/tree/main/docs/TESTS/Test2_DeepLabV3%2B

4. Cityscapes metrics

In different studies that compare semantic segmentation models that I have been able to read (among them cityscapes), in addition to the conventional metrics (F1-score, TP, TN, FP, FN), the IoU (Interception over Union) metrics are usually used, both for classes as categories.

Next Week Work Planning

  1. Continue adapting DeepLabV3+ and GanAV with the RELLIS-3D and RUGD datasets.
  2. GitHub Pages cloning repository.
  3. Create post for the current and the last 3 weeks work progress.

References

  • [Cityscapes Metrics] https://www.cityscapes-dataset.com/benchmarks/#instance-level-scene-labeling-task