Comments (2)
Glad to hear that our method brings performance improvement. The current implementation adopts the online label refinement instead of calculating and updating the label after each cycle, which aims to code simplicity and readability. There will only be a negligible performance gap.
from self-correction-human-parsing.
Thanks!
from self-correction-human-parsing.
Related Issues (20)
- How can i run it on videos ? HOT 1
- LIP dataset not available HOT 3
- How to visualize edge prediction? HOT 1
- Where is the pre-trained model for Multiple Human Parsing?
- Training Time is huge
- CIHP dataset Training
- Testing on CPU HOT 12
- MulitHuman Parsing Resutls
- How to trained on custom dataset? HOT 2
- LIP dataset - crashed link HOT 1
- CUDA problem on training HOT 1
- generating human-parsing HOT 1
- Hi! I want to convert model to onnx but i have some error with InPlaceABNSync. can you give me some solutions. My error "RuntimeError: ONNX export failed: Couldn't export Python operator InPlaceABNSyn"
- Pascal-Person-Part求助
- Training with others backbone
- Excuse me, why is it stuck here all the time?I need help.
- Real-time test problem
- Cannot reproduce the results from the paper - Problem with InPlaceABNSync?
- 我下载到本地后,在pycharm上运行出的结果和colab上边运行的结果不一样是为啥呀??都是按照colab的步骤做的呀! HOT 1
- Which torch and torchvision version are appropriate? HOT 1
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from self-correction-human-parsing.