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Use F1-Score, Accuracy etc. to evaluate the performance of the model.
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Hey, could you please talk about the performance metrics of this pytorch implementation?
Thanks
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First of all thank you for the paper and codes on github. I read the paper but could not understand the performance metrics. Could you please explain more clearly the statements precision and recall? …
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Code to check the performance of different models.
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We want to get baseline metrics for fastest possible performance, so we will set up an instance in AWS' us-west-2 region and run the following tests:
- Large numbers of files (e.g. 100, 500, 1000)
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```python
# setup loop with TQDM and dataloader
loop = tqdm(test_dl, leave=True)
# setup epoch's metrics
metrics = {'losses': [], 'accuracy': [], 'AUC': []}
for step, (img1, img2, labels) in enum…
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Hi all,
q1) what is the reason behind focusing on T_eff and not on Gpts/s as commonly used in papers reporting stencil performance?
q2) Figure 2 shows that using the math-close notation, perform…
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Recently, I find there are several proposals about system benchmarks, e.g., #1003 and #889 , which seem to have similar tests and make me confused. So I did a simple research about them.
### Overvi…
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- Part of dask/distributed#7665
In the demo of dask/distributed#7586, you can spot "I/O" time in the collected metrics. This was possible by decorating I/O heavy dask/dask functions:
```patch
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