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First off, thank you for the insightful paper and for releasing your code. I would like to use your model(s) to ultimately paraphrase some custom datasets for machine translation tasks.
I was wonde…
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##### System information (version)
- OpenVINO=> 2021.2
- Operating System / Platform => Windows 64 Bit
- Compiler => Visual Studio 2019
- Problem classification => Model Compilation
- Framework…
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Hi,
Thanks a lot for this great repo.
For the comparison in the Visualizations example, I found that for each config, you run 100 updates.
I am concerned that 100 is too small so that it would fa…
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I have git cloned the repository and run `./setup.py install` and `./setup-dataset.sh`, but then I realized `train_all.sh` was not present. Later I found it in the 4.1.3 release. Do I need to set up o…
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**Describe the Bug**
I am working on Google Colab
Error:
AttributeError Traceback (most recent call last)
in ()
1 from keras_adabound import AdaBound
----> 2…
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I noticed in your code you tried using Adabound. How did it compare to SGD + cosine annealing + burnin? Presumably you didn't need the burnin for Adabound?
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Have you ever trained the model for CoCo dataset without weight decay?
When I trained and tested my model with custom data, using weight decay method harm performance.
Best mAP during 68 epochs
…
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Hi !
First, great implementation, much better than most out there when it comes to code clarity.
I am running into some small issues:
When dataset entries are handed of too albumentations, albume…
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Incremental PCA is already [implemented in scikit-learn](https://scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html) but seems like it is straightforward to implement. See […
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In the following code:
```
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2 += [v] # bi…