3 ms·
Sounds impressive. But please allow me to rant here: > are more accurate Can we please stop using “accurate” as an adjective for models, especially when repor
by nirushiv 6y ago
Sounds impressive. But please allow me to rant here:
> are more accurate
Can we please stop using “accurate” as an adjective for models, especially when reporting on studies?
There are many other more telling metrics. Let’s start with precision and recall, and diagnostic ability. “Accuracy” tells nothing about the quality of a model.
/rant
- seesawtron 6y agoThe paper uses Dice Coefficient for the evaluation of their model which is common in such semantic segmentation tasks. The article is written for lay audience hence the use of "accuracy". Dice Coefficient = 2 * the Area of Overlap divided by the total number of pixels in both images. 0. https://www.nature.com/articles/s41467-020-19449-7#Abs1 https://www.nature.com/articles/s41467-020-19449-7#Abs1 1. https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2 https://towardsdatascience.com/metrics-to-evaluate-your-sema...
- nirushiv 6y agoThanks for digging deeper. I saw that it was from an academic institution (TUM) so the lack of useful metrics in the release admittedly ticked me off a little. Dice Coeff sounds very similar to Jacard Indexing for image tasks.