update nuclei broad example

Author: FynnBe

example_specs/models/unet2d_nuclei_broad/rdf_v0_4_9.yaml

@@ -0,0 +1,93 @@
+format_version: 0.5.0
+
+name: UNet 2D Nuclei Broad
+description: A 2d U-Net trained on the nuclei broad dataset.
+authors:
+- name: "Constantin Pape;@bioimage-io"
+  affiliation: "EMBL Heidelberg"
+  orcid: "0000-0001-6562-7187"
+- name: "Fynn Beuttenmueller"
+  affiliation: "EMBL Heidelberg"
+  orcid: "0000-0002-8567-6389"
+maintainers:
+- name: "Constantin Pape"
+  github_user: constantinpape
+
+
+# we allow for multiple citations. Each citation contains TEXT, DOI and URL. One of DOI or URL needs to be given.
+cite:
+- text: "Ronneberger, Olaf et al. U-net: Convolutional networks for biomedical image segmentation. MICCAI 2015."
+  doi: https://doi.org/10.1007/978-3-319-24574-4_28
+- text: "2018 Data Science Bowl"
+  url: https://www.kaggle.com/c/data-science-bowl-2018
+
+git_repo: https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_specs/models/unet2d_nuclei_broad
+tags: [unet2d, pytorch, nucleus, segmentation, dsb2018]
+license: MIT
+
+documentation: README.md  # may also be a url
+covers: [cover0.png]
+attachments: {}
+timestamp: 2019-12-11T12:22:32Z  # ISO 8601
+
+inputs:
+- name: raw
+  description: raw input
+  shape: [1, 1, 512, 512]
+  axes:
+  - role: batch
+  - role: channel
+  - role: y
+  - role: x
+  data_type: float32
+  data_range: [-.inf, .inf]
+  test_tensor: test_input.npy
+  sample_tensor: test_input.npy
+  preprocessing:  # list of preprocessing steps
+    - name: zero_mean_unit_variance  # name of preprocessing step
+      kwargs:
+        mode: per_sample      # mode in [fixed, per_dataset, per_sample]
+        axes: yx              # subset of axes to normalize jointly, batch ('b') is not a valid axis key here!
+
+outputs:
+- name: probability
+  description: probability in [0,1]
+  axes: bcyx
+  data_type: float32
+  data_range: [-.inf, .inf]
+  halo: [0, 0, 32, 32]
+  test_tensor: test_output.npy
+  sample_tensor: test_output.npy
+  shape:
+    reference_tensor: raw
+    scale: [1.0, 1.0, 1.0, 1.0]
+    offset: [0.0, 0.0, 0.0, 0.0]
+
+weights:
+ pytorch_state_dict:
+   authors:
+     - name: "Constantin Pape;@bioimage-io"
+       affiliation: "EMBL Heidelberg"
+       orcid: "0000-0001-6562-7187"
+   sha256: e4d3885bccbe41cbf6c1d825f3cd2b707c7021ead5593156007e407a16b27cf2
+   source: https://zenodo.org/record/3446812/files/unet2d_weights.torch
+   architecture: unet2d.py:UNet2d
+   architecture_sha256: cf42a6d86adeb4eb6e8e37b539a20e5413866b183bed88f4e2e26ad1639761ed
+   kwargs: {input_channels: 1, output_channels: 1}
+   dependencies: conda:environment.yaml
+ onnx:
+   sha256: f1f086d5e340f9d4d7001a1b62a2b835f9b87a2fb5452c4fe7d8cc821bdf539c
+   source: weights.onnx
+   opset_version: 12
+   parent: https://zenodo.org/record/3446812/files/unet2d_weights.torch
+ torchscript:
+   sha256: 62fa1c39923bee7d58a192277e0dd58f2da9ee810662addadd0f44a3784d9210
+   source: weights.pt
+   parent: https://zenodo.org/record/3446812/files/unet2d_weights.torch
+
+type: model
+version: 0.1.3
+download_url: https://example.com  # note: not recommended for model RDFs
+
+training_data:
+  id: ilastik/covid_if_training_data  # note: not the real training data