Comments (2)
Hi @bakachan19,
Thank you for the question. This sample size should be smaller than the number of total samples. It also should be manageable to avoid OOM. For example, given a dataset with 50k samples, 70% of the total number, i.e., 70% * 50k = 35k, would be a good choice for both performance and your CPU memory. Although the results are not completely the same by changing the sample size, the overall performance is not sensitive to the sample size. For example, the F1 score of detection should be similar for experiments with sample_size=15000
and sample_size=35000
.
Please feel to let us know if you have any questions.
Best,
Zhaowei
from docta.
Thank you for the answer @zwzhu-d.
from docta.
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- image preprocessing HOT 4
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