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GSoC 2021 project
There few basic requirements for new contributors. They have to accomplish the following steps:
- read project's contributing guide and setup development environment
- learn the concepts of the project
- run some of our examples
To be considered as a GSoC student, it is necessary to have at least 3 accepted PRs to the project. They can something small like a doc fix or a simple bug fix. Please, see help wanted issues.
Metrics module in PyTorch-Ignite proposes very unique features to evaluate a trained PyTorch model in an online manner and for any type of computation settings (single process or distributed configuration). Namely, the module provides essential metrics for classification/segmentation tasks and various regression metrics.
The plan for the improvements contains the following topics:
- All metrics should work in distributed configuration (#1284)
- explore asymmetric distributed metrics computation. PyTorch uneven distributed input support.
- provide configurable distributed metrics reduce/gather methods (#1242)
- Metrics for object detection task: implement mean Average Precision metric
- Metrics for NLP: essential metrics for common tasks: ROUGE, BLEU, etc
- Metrics for GANs: FID, PPL, others (see #998)
- Add minor improvements:
- better support of sklearn metrics
- classification metrics with micro/macro options
Library provides a very flexible way to construct a model's trainer, however this API can be optionally simplified further for a number of common tasks (#912).
- Implement a Higher-level API
- We can explore a solution with
Model.fitAPI as here.
- We can explore a solution with
- Provide contrib trainers with
- Create new examples using this Higher-level API
PyTorch-Ignite presented to you with love by PyTorch community