Update BasicUNet-Pathology nuclick annotation

[bioimageiobot]

This is an automatic PR created by the @bioimageiobot regarding changes to the resource item 10.5281/zenodo.8356515.
The following version(s) will be added:

• 8358778 (version 2)

Please review the changes and make sure the new item or version(s) pass the following check list:

• Passed the bioimage.io CI tests: static (and dynamic) validations
• The meta information for the RDF item is complete
  • The tags are complete and describe the model
  • Naming is intuitive and descriptive, example: Multi-Organ Nucleus Segmentation (StarDist 2D)
  • Authors are provided
  • Documentation is complete
    • For models, include an overview, describe how the model is trained, what is the training data, how to use the model, how to validate the results and list the references. TODO: Model documentation template.
• Approved by at least one of the bioimage.io admin team member.

Maintainers: @IvanHCenalmor

Note: If you updated or re-uploaded another version for the current item on Zenodo, this PR won't be changed automatically. To proceed, you can do the following:

1. Block this version, but keep looking for future versions: Edit the current resource.yaml and keep the top-level status field as accepted, but change the status under the current version to blocked.
2. Accept this version and keep looking for future versions: Merge this PR for now.
3. Keep proposed version(s) (and this resource in general if it is new) as pending: Close this PR without merging.

Then wait for the CI on the main branch to complete. It should detect the new version(s) and create another PR for the new version(s).

Previous PRs of this resource: #637

[IvanHCenalmor]

This time I ran the bioimageio validate _rdf_file_ command and it returned that no errors were found. Seems like the prediction is not the same no? I will try to run the model build another time from zero to see if that solves the problem.

[FynnBe]

This time I ran the bioimageio validate _rdf_file_ command and it returned that no errors were found. Seems like the prediction is not the same no? I will try to run the model build another time from zero to see if that solves the problem.

Mismatched elements: 12 / 16384 (0.0732%

don't worry about this.. we should accept it. I'll reduce the test precision.

[FynnBe]

@IvanHCenalmor Also with the lightning dependency it would be nice to add the pytorch_state_dict weights as well. For reference, but also as your torchscript model is traced (if I'm not mistaken) then having the "raw" pytorch model might be useful to some.

[bioimageiobot]

preview