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First of all, thanks a lot for sharing this great project. I want to use this model to predict the scenes on COCO dataset, however, I'm confused about the format of the top3 scores in the precomputed dataset. Why is the highest score of a pixel about 0.4 but 1. what function do you use to get these scores?
Hi! This question is further discussed in the paper, in case you want to take a look.
In summary, if you have an RGB image as input for the Semantic Segmentation Network you will end up having a probability distribution of semantic scores for each pixel. This means that if you have 120 semantic classes, for each pixel you will have a distribution of 120 scores. The sum of all that scores must be 1 (probability distribution) but this does not mean that the top score must be 1. You can have 120 scores summing up to 1 with the highest one being 0.4.
Hi! This question is further discussed in the paper, in case you want to take a look.
In summary, if you have an RGB image as input for the Semantic Segmentation Network you will end up having a probability distribution of semantic scores for each pixel. This means that if you have 120 semantic classes, for each pixel you will have a distribution of 120 scores. The sum of all that scores must be 1 (probability distribution) but this does not mean that the top score must be 1. You can have 120 scores summing up to 1 with the highest one being 0.4.
First of all, thanks a lot for sharing this great project. I want to use this model to predict the scenes on COCO dataset, however, I'm confused about the format of the top3 scores in the precomputed dataset. Why is the highest score of a pixel about 0.4 but 1. what function do you use to get these scores?
In https://github.com/CSAILVision/semantic-segmentation-pytorch the scores are product by softmax.
Thanks a lot!
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