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.
Decode Trail Latest Questions
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 ...
My speech-to-text model produces accurate transcripts when tested in a quiet office.However, when I try to use it in public places, accuracy drops sharply.Background noise causes words to be skipped or misheard.The model feels fragile outside controlled ...
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.
My GAN generates images but they look washed out.Many samples look almost identical.Training loss looks stable.But the visual quality never improves.
I am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
I upgraded to a GPU with much more VRAM.I increased the batch size to use the available memory.Now the training is noticeably slower per epoch.There are no errors, but performance feels worse than before.
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.
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.
My diagnostic CNN shows high accuracy on data from one hospital.When tested on scans from a different hospital, performance drops drastically.The disease patterns are the same.Only the scanners and imaging pipelines differ.