| device cpu | train (178614, 49, 9, 1) (75 MB uint8) | val (4941, 49, 9, 1) | bins 49 win 9 |
| params: 338,302 |
| ep 0 loss 7.7432 val cell-acc 0.7552 val note(str+fret)-acc 0.1933 |
| ep 2 loss 2.5779 val cell-acc 0.8684 val note(str+fret)-acc 0.6916 |
| ep 4 loss 2.1270 val cell-acc 0.8852 val note(str+fret)-acc 0.7657 |
| ep 6 loss 1.9533 val cell-acc 0.8864 val note(str+fret)-acc 0.7722 |
| ep 8 loss 1.8558 val cell-acc 0.8946 val note(str+fret)-acc 0.7953 |
| ep 10 loss 1.7913 val cell-acc 0.8923 val note(str+fret)-acc 0.7773 |
| ep 12 loss 1.7444 val cell-acc 0.8918 val note(str+fret)-acc 0.7820 |
| ep 14 loss 1.7042 val cell-acc 0.8970 val note(str+fret)-acc 0.8084 |
| ep 16 loss 1.6539 val cell-acc 0.8967 val note(str+fret)-acc 0.7938 |
| ep 18 loss 1.6371 val cell-acc 0.8923 val note(str+fret)-acc 0.7851 |
| ep 20 loss 1.6228 val cell-acc 0.8914 val note(str+fret)-acc 0.7878 |
| ep 21 loss 1.6154 val cell-acc 0.8967 val note(str+fret)-acc 0.7867 |
| /Users/christianstrobele/code/onnx_runtime_dart/tool/tab_labeler/train.py:209: DeprecationWarning: You are using the legacy TorchScript-based ONNX export. Starting in PyTorch 2.9, the new torch.export-based ONNX exporter has become the default. Learn more about the new export logic: https://docs.pytorch.org/docs/stable/onnx_export.html. For exporting control flow: https://pytorch.org/tutorials/beginner/onnx/export_control_flow_model_to_onnx_tutorial.html |
| torch.onnx.export( |
| FINAL (best ckpt) train note-acc 0.8261 val note-acc 0.8084 (best val 0.8084) |
| exported /private/tmp/claude-501/-Users-christianstrobele-code-onnx-runtime-dart/9356a989-c7fb-455b-b6f8-bfa444e4d5b3/scratchpad/tab_labeler/rerun-20260721-105128/tab-labeler.onnx |
| wrote parity fixture (240 examples) |
|
|
