My model recognizes actions well in static camera videos.When the camera pans or shakes, predictions become unstable.The action is the same.Only the camera motion changes.
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critical error on the website
The base model worked well before.After fine-tuning on new data, accuracy drops everywhere.Even old categories are misclassified.The model seems to have forgotten what it knew.
The reconstruction loss is very low on training images.But when I test on new data, outputs look distorted.The model seems confident but wrong.It feels like it memorized the dataset.
Even small changes need extensive testing. I want to understand why.
I added thousands of new user interactions to my training dataset.Instead of improving, the recommendation quality dropped.Users are now getting irrelevant suggestions.It feels like more data made the model less accurate.
A WordPress site and its firewall show that brute-force protection is enabled.Attackers are making thousands of login attempts from different IPs.No IPs are getting banned, and the logs show everything as “allowed.”The site is running behind a ...
A PyTorch inference script produces different outputs on every run.The model weights are loaded from the same file and the input tensor never changes.This only happens after moving from training to deployment.There are no errors or warnings.
Test classes that were once simple now require extensive setup and complex assertions. Small changes in automation break multiple tests. Maintaining coverage feels increasingly expensive. I want to understand why this happens and how teams manage it.
We collect logs, but during incidents they don’t answer key questions.Important details seem to be missing or hard to correlate.I’m trying to understand how to make logs more useful!