Comments (2)
Hi!
We don't provide a specific Semantic Segmentation code to obtain the semantics, we just provide the semantic segmentations extracted by PSPNet used to obtain the results from the paper.
You can use any Semantic Segmentation CNN of your choice. You just have to save the top3 labels and top3 scores leveraging the 3 channels of an RGB image.
Alex.
from semantic-aware-scene-recognition.
how can i modify code to input a image and get the semantics as output
from semantic-aware-scene-recognition.
Related Issues (20)
- Model zoo links expired HOT 6
- AttributeError: Can't pickle local object 'ADE20KDataset.__init__.<locals>.<lambda>' HOT 3
- question about the format of the top3 scores in the precomputed dataset. HOT 2
- resnet50-RGB-branch model
- how to train your model. Need the train.py file or the command to train HOT 8
- How to convert semantic segmentation results into required format HOT 5
- 如何测试但张图片? HOT 2
- Thank you! HOT 4
- I tried to train the RGB branch on ADE20K from scratch, but only got 45% acc rather than 55.9%. HOT 13
- Runtime Error while evaluating the model HOT 1
- How to test this model? HOT 1
- no question
- Training Problems
- The training code
- Question about two attention modules HOT 4
- Why did the semantic score map become 152 channels HOT 3
- Link Down: Cannot downloads Weight files and nois_semantic data HOT 1
- When I use RGB_ ResNet50_ SUN model, select ONLY_ RGB: TRUE, the evaluation.py report an error HOT 3
- Places-365 noisy training data not found HOT 1
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from semantic-aware-scene-recognition.