Please provide the following information when requesting support.
• Hardware RTX3090
• Network Type unet / segformer
I have an existing unet model that has this performance:
“{‘foreground’: {‘precision’: 0.9996697, ‘Recall’: 0.9997607, ‘F1 Score’: 0.999715147331038, ‘iou’: 0.99943054}, ‘background’: {‘precision’: 0.6680363, ‘Recall’: 0.59312135, ‘F1 Score’: 0.6283537780304688, ‘iou’: 0.45810193}}”
This is the spec file for unet:
unet_train_resnet_6S300.txt (1.3 KB)
Using the same dataset of 1280 X 704 Grayscale images, and mask values either 0 or 255, I am looking to see if performance can be improved by training a segformer with this spec file (modified version of the isbi example):
train_isbi.yaml (1.4 KB)
Training with the segformer notebook, the Groundtruth Masks are visualized properly, and the training result is Very bad as there is no detection at all of the foreground class!!
2023-02-17 00:07:52,630 - mmseg - INFO - per class results:
2023-02-17 00:07:52,631 - mmseg - INFO -
+------------+-------+-------+
| Class | IoU | Acc |
+------------+-------+-------+
| background | 99.92 | 100.0 |
| foreground | 0.0 | 0.0 |
+------------+-------+-------+
2023-02-17 00:07:52,631 - mmseg - INFO - Summary:
2023-02-17 00:07:52,631 - mmseg - INFO -
+--------+-------+------+-------+
| Scope | mIoU | mAcc | aAcc |
+--------+-------+------+-------+
| global | 49.96 | 50.0 | 99.92 |
+--------+-------+------+-------+
2023-02-17 00:07:52,694 - mmseg - INFO - Iter(val) [1000] mIoU: 0.4996, mAcc: 0.5000, aAcc: 0.9992
Telemetry data couldn't be sent, but the command ran successfully.
[Error]: <urlopen error [Errno -2] Name or service not known>
Execution status: PASS
2023-02-17 02:07:59,748 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.
The complete training log is here:
segformer training output 2023 02 17.txt (18.8 KB)
Foreground has performance of 0.0 !
Thanks for the help
Dave