Your submission is now in Draft mode.

Once it's ready, please submit your draft for review by our team of Community Moderators. Thank you!

Submit Essay

Once you submit your essay, you can no longer edit it.


This content now needs to be approved by community moderators.


This essay was submitted and is waiting for review.

SOTA on PASCAL Context on 2023-02-14


Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category (Thoma, 2016).

The PASCAL-Context dataset is a challenging scene parsing dataset that contains 59 semantic classes and 1 background class (Mottaghi et al., 2014). The training set and test set consist of 4, 998 and 5,105 images respectively.

As of writing this question, the state-of-the-art model for semantic segmentation on PASCAL-Context is ResNeSt-269 (Zhang et al., 2020), which achieves 58.92 mIoU% (see their detailed results here).

An excellent reference for tracking state-of-the-art models is PapersWithCode, which tracks performance data of ML models.

What will the state-of-the-art performance on semantic segmentation of PASCAL-Context be on 2023-02-14 in mean IoU in percent (MIoU%), amongst models not trained on extra data?

This question resolves as the highest level of performance (in MIoU%) achieved on the PASCAL-Context (2014) dataset up until 2023-02-14, 11:59PM GMT amongst models trained on only the PASCAL-Context training set—no extra training data may be used. The model's level of performance is to be evaluated on the PASCAL-Context test set.

Performance figures may be taken from e-prints, conference papers, peer-reviewed articles, and blog articles by reputable AI labs (including the associated code repositories). Published performance figures must be available before 2023-02-14, 11:59PM GMT to qualify.

Make a Prediction


Note: this question resolved before its original close time. All of your predictions came after the resolution, so you did not gain (or lose) any points for it.

Note: this question resolved before its original close time. You earned points up until the question resolution, but not afterwards.

Current points depend on your prediction, the community's prediction, and the result. Your total earned points are averaged over the lifetime of the question, so predict early to get as many points as possible! See the FAQ.