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RTX3090 Ubuntu 18.04
TAO: dockers: [‘nvidia/tao/tao-toolkit-tf’, ‘nvidia/tao/tao-toolkit-pyt’, ‘nvidia/tao/tao-toolkit-lm’] format_version: 2.0 toolkit_version: 3.22.02 published_date: 02/28/2022
spec file unet_train_resnet_unet_6S.txt (1.4 KB)
Images and masks inside data.zip can be found here: github.com/DaveBGld/TAO.git
To reproduce the problem, in the cv_samples_v1.3.0/unet/unet_isbi.ipynb
example, replace the spec file contet and the data folders with the above.
After running tao unet evaluate
the results_tlt.json
is:
“{‘Background’: {‘precision’: 0.97684205, ‘Recall’: 1.0, ‘F1 Score’: 0.9882853795702129, ‘iou’: 0.97684205}, ‘Plant2’: {‘precision’: nan, ‘Recall’: 0.0, ‘F1 Score’: nan, ‘iou’: 0.0}, ‘Leaf’: {‘precision’: nan, ‘Recall’: 0.0, ‘F1 Score’: nan, ‘iou’: 0.0}}”
These are color images in PNG files, 512X512, with masks also in PNG files 512X512, where ALL pixels in the masks fall under {0, 1, 2} for {Background, Plant2, Leaf}
Following Morganh advice in this post Fail with Transfer Learning with Unet Multiclass, Color Images, Semantic Segmentation - #18 by david9xqqb I have in the specs
loss: “cross_entropy”
weight: 2e-06
crop_and_resize_prob : 0.01
Please see my first reply for additional info (did not allow me more than 3 links!!!)
Clueless on how to proceed
Many thanks!