Comments (2)
在我修改的其他的实验中,epoch=40时往往比最终结果要高1%左右,并且我在数据预处理中将Temporal Scaling去掉了,所以baseline结果应该更高一些,而不是更低
期待您的回复
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HI, @miaomiao9miao
As shown in our recent update, the original implementation performs worse when adopting multiple GPU due to nn.Datapallel. Therefore, we adopt syncBN in our later experiments, and we release some experimental results in this update, more results will be released in our future journal version.
As you adopting single GPU (also modifying the batch size), which is expected to perform better than our previous conference version. We also release an updated baseline with 40 epochs training, and you can adopt this setting for comparison.
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Related Issues (20)
- Error when i run the training part main.py HOT 1
- unable to run successfully when I run main.py HOT 1
- unable to successfully install CTC HOT 5
- Detailed code running steps HOT 19
- Visualizing the predicted alignments HOT 1
- unable to run these repository on google colab HOT 1
- Data augmention error HOT 4
- about the feature extractor architecture HOT 6
- Successfully inference, but unable to train HOT 3
- Visualize Epoch vs Loss HOT 1
- Hardware and Software Specifications for this research. HOT 3
- Gloss Segment Boundary Assignment Algorithm (GSBA) HOT 1
- Does your PHOENIX-2014-T dataset have /1/ folder? HOT 1
- model or model.parameters()? HOT 2
- Does your model produce different results each time it is run? HOT 4
- Training the baseline (without VAC, SMKD) HOT 2
- How can I use a trained model to test a video? HOT 1
- unable to reconize any word but the loss is decreasing??? HOT 1
- Does preprocess.sh have an effect if using datasets other than phoneix? HOT 1
- IndexError in DataLoader Worker Process with Custom Dataset
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