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, ...
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My image classifier performs very well on bright daylight photos.When images are darker or taken indoors, accuracy drops sharply.The objects are still the same.Only the lighting seems different.
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.
The model produces grammatically correct text.But it keeps repeating the same phrases.The output never moves forward.It feels stuck in a loop?
My CNN reaches over 95% accuracy on the training set.But on the test set it drops below 40%.The data comes from the same source.I feel the model is memorizing instead of learning.
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.
I trained an LSTM for next-word prediction on text data.The training loss decreases normally.But when I generate text, it repeats the same token again and again.It feels like the model is ignoring the sentence.
My model uses both image and text inputs.It works well when both are provided.If one modality is missing, outputs become random or broken.Real-world data is often incomplete.
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.