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Hi, I have a conceptual question wether I should split my dataset in train/test or not.
Given the fact that my dataset has no labels, does it make any sense to split in the first place? I mean, I cou…
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Hello every body
I 'm using cross validation(cv) for a classification problem, I spilt my data into test and train, I used train for cv model, and test data for inference step.
my code for cv and ea…
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Hi
I am a graduate student from the University of Science and Technology of China. Thank you very much for your contributions in the field, and I am now reproducing this work. I got the data set acc…
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While using the model for classification, a ValueError is raised due to a mismatch between the feature na…
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Hello, I would like to reproduce your results, and I was wondering if you could give me some information about how did you split the dataset in train,val an test sets.
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Hello ， when I try to train the model using blendedmvs, it core in losses.py
assert valid_matches.shape == torch.Size([B, N]) and valid_matches.sum() > 0
can you help me solve it,
the train…
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### Is there an existing issue for this?
- [x] I have searched the existing issues
### Operating system
- [x] Linux
- [ ] Mac
- [ ] Windows
### Operating system version
For example one of the fol…
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Hi,
This is an interesting job, I would like to do some initial experiments, however I realized the splits are not so balance between training and testing. I mean I saw around 35k sequences for tra…
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scikit-learn implementation of train test split (sklearn.model_selection.train_test_split) supports splitting data according to class labels (stratified split) by using the argument `stratify`. This i…
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In the config split is mentioned:
train_csv: /data/hse/data/train.csv
test_csv: /data/hse/data/val.csv
Looks like these files shall be commited to make the results reproducible?