I trained a CNN to classify multiple object categories from images.The training completes without errors and the accuracy looks decent.But when I run inference, every image gets the same label.Even very different images are predicted as the ...
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The model produces grammatically correct text.But it keeps repeating the same phrases.The output never moves forward.It feels stuck in a loop?
The system performs well in offline tests.Under real user traffic, errors appear.Latency increases and predictions degrade.The same model is running.
My model gives great accuracy on my laptop.When deployed on a server, predictions become inconsistent.The same input sometimes produces different outputs.Nothing crashes, but the behavior is unreliable.
The model classifies upright images correctly.Rotated versions of the same images fail.The content is the same.Only orientation changed.
My RNN works fine on short sequences.When I give it longer inputs, predictions become random.Loss increases with sequence length.It feels like the model forgets earlier information.
Short sequences work fine.Longer sequences cause GPU crashes.No code changes were made.Only input size increased.
I fine-tuned a pretrained Transformer on a small custom dataset.Training finishes without errors.But the generated outputs look random and off-topic.It feels like the model forgot everything.
I trained an object detection model on a mixed dataset containing people, vehicles, and small objects like phones and traffic signs.The model detects large objects such as cars and people very reliably.However, it almost completely ignores smaller objects, ...