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Imbalanced datasets, where the classes have very different occurrence rates, can show up in large data sets.
There are many strategies for dealing with imbalanced data. http://contrib.scikit-learn.…
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[Focal loss ](https://www.tensorflow.org/addons/api_docs/python/tfa/losses/SigmoidFocalCrossEntropy) may be useful to our classification use case, where there is a significant imbalance between classe…
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请问作者有没有尝试直接在原版的[deepsnake](https://github.com/zju3dv/snake)里加入ConvGRU模块。
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- The ECAPA-TDNN model trained on "development part of the `VoxCeleb2`".
- The datasets contains 5994 speakers.
- Some speakers have many more files than other speakers (`imbalanced classes)`.
- …
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### Question
Hi,
I use yolov5x with this setting (train: 70%, val: 10%, test: 20%)
```
train: images/train # train images (relative to 'path') 128 images
val: images/val # val images (relativ…
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Hello, thanks for this repo,
about this line:
```
mask=torch.where(mask==self.subclasses[self.tmp_index-1], self.tmp_index, 0)
```
https://github.com/marcoaversa/diffinfinite/blob/master/datase…
joihn updated
1 month ago
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Dear Mr. Wang,
Hope this issue find you well.
May I ask for a issue for the training process? How to solve the imbalance of categories in the training data set? I'm failed. the positive class an…
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### Search before asking
- [X] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussion…
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We have a very imbalanced machine learning problem, where we have far fewer SecureDrop users than non-SecureDrop users. There are many ways of handling this situation - including oversampling the mino…
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The xgboost, RF and NN models all have different ways to handle imbalanced classification datasets by using class-specific weights in their loss functions; but we currently only support this for NN mo…