https://github.com/nasa-nccs-hpda/rst-anomaly-detection
Roman Space Telescope Anomaly Detection
https://github.com/nasa-nccs-hpda/rst-anomaly-detection
ai anomaly-detection gpu mask-rcnn ml pytorch
Last synced: about 2 months ago
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Roman Space Telescope Anomaly Detection
- Host: GitHub
- URL: https://github.com/nasa-nccs-hpda/rst-anomaly-detection
- Owner: nasa-nccs-hpda
- Created: 2022-02-03T17:23:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-02T14:24:14.000Z (almost 2 years ago)
- Last Synced: 2025-03-28T13:46:12.396Z (7 months ago)
- Topics: ai, anomaly-detection, gpu, mask-rcnn, ml, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 3.09 MB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# rst-anomaly-detection
[](https://github.com/psf/black)
Python library to discover anomalies in Roman Space Telescope (RST) simulated data by means of GPUs and CPU parallelization
for high performance and commodity base environments. This repository focuses in using CNNs for the identification
of anomalies in RST sensor imagery.
## Science Questions
- Uncover anomalies in RST simulated data
- Understand sensor anomalies and plan for the future of the mission
- Use deep learning models (segmentation and object detection) to analyze anomalies in RST data
## Download
- Specific Version: Under releases, hit download to the specific version you wish to download.
- Latest Version:
```bash
wget https://raw.githubusercontent.com/nasa-nccs-hpda/rst-anomaly-detection/main/notebooks/RST_MaskRCNN.ipynb
```
## Login to ADAPT JupyterHub
To leverage NCCS ADAPT resources, you will need to login to ADAPT JupyterHub. The steps are outlined below.
1. Login to the NCCS JupyterHub .
2. Open this notebook via the file/upload method.
3. Select kernel, in this case "ilab-pytorch".
4. Start working on your notebook.
## Authors
- Jordan Alexis Caraballo-Vega, jordan.a.caraballo-vega@nasa.gov
## References
[1] Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.
[2] Paszke, Adam; Gross, Sam; Chintala, Soumith; Chanan, Gregory; et all, PyTorch, (2016), GitHub repository, . Accessed 13 February 2020.
[3] Caraballo-Vega, J., Carroll, M., Li, J., & Duffy, D. (2021, December). Towards Scalable & GPU Accelerated Earth Science Imagery Processing: An AI/ML Case Study. In AGU Fall Meeting 2021. AGU.