MetOffice / MetOffice/XBTs_classification
Set up classification pipeline in pytorch
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
We would like to produce classification results in the output dataset that have probability distributions associated with the results. This way the distribution can be sampled in the process of creating ocean temperature dataset (EN4 https://www.metoffice.gov.uk/hadobs/en4/ ) that will be making use of these results. Pytorch deals with probabilities intrinsically and increasing rapidly in use in such tasks. As such it would be good to evaulate it for the XBT classification task.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files or tests are named. Start by locating the existing XBT classification entry point and reviewing how classification results are written to the output dataset. Done means the pipeline produces probability distributions that can be sampled when creating the EN4 ocean-temperature dataset.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pytorch
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100