zhanghang1989 / zhanghang1989/PyTorch-Encoding
ADE20K validation accuracy
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- Dominant language
- Python
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Description
Using this command
python test.py --backbone resnet101 --base-size 640 --crop-size 576 --model-zoo EncNet_ResNet101_ADE --eval
gives me 44.31% mIoU as opposed to 44.4% reported here or 44.65% in the paper.
Am I missing anything here? Thanks!
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the command shown in test.py with the EncNet_ResNet101_ADE model and compare its 44.31% mIoU with the 44.4% result in the linked experiment report and 44.65% in the paper. Check whether the command or evaluation setup differs from those references; done means explaining the discrepancy or identifying the configuration needed to reproduce the reported accuracy.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100