[System]
You are a helpful assistant that answers strictly in JSON. Here's the JSON schema you must adhere to:
<schema>
{"$defs":{"ArchitectureFromFileDescr":{"additionalProperties":false,"properties":{"source":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"}],"description":"Architecture source file","title":"Source"},"sha256":{"anyOf":[{"description":"A SHA-256 hash value","maxLength":64,"minLength":64,"title":"Sha256","type":"string"},{"type":"null"}],"default":null,"description":"SHA256 hash value of the **source** file.","title":"Sha256"},"callable":{"description":"Identifier of the callable that returns a torch.nn.Module instance.","examples":["MyNetworkClass","get_my_model"],"minLength":1,"title":"Identifier","type":"string"},"kwargs":{"additionalProperties":{"$ref":"#/$defs/YamlValue"},"description":"key word arguments for the 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max_percentile: 99.8\n              min_percentile: 5.0\n          - id: clip\n              kwargs:\n              min: 0.0\n              max: 1.0\n          - id: ensure_dtype  # the neural network of the model requires uint8\n              kwargs:\n              dtype: uint8\n        ```\n    - in Python:\n        >>> preprocessing = [\n        ...     ScaleRangeDescr(\n        ...         kwargs=ScaleRangeKwargs(\n        ...           axes= (AxisId('y'), AxisId('x')),\n        ...           max_percentile= 99.8,\n        ...           min_percentile= 5.0,\n        ...         )\n        ...     ),\n        ...     ClipDescr(kwargs=ClipKwargs(min=0.0, max=1.0)),\n        ...     EnsureDtypeDescr(kwargs=EnsureDtypeKwargs(dtype=\"uint8\")),\n        ... ]","properties":{"id":{"const":"ensure_dtype","title":"Id","type":"string"},"kwargs":{"$ref":"#/$defs/EnsureDtypeKwargs"}},"required":["id","kwargs"],"title":"model.v0_5.EnsureDtypeDescr","type":"object"},"EnsureDtypeKwargs":{"additionalProperties":false,"description":"key word arguments for `EnsureDtypeDescr`","properties":{"dtype":{"enum":["float32","float64","uint8","int8","uint16","int16","uint32","int32","uint64","int64","bool"],"title":"Dtype","type":"string"}},"required":["dtype"],"title":"model.v0_5.EnsureDtypeKwargs","type":"object"},"FileDescr":{"additionalProperties":false,"description":"A file description","properties":{"source":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"}],"description":"File 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YAML\n    ```yaml\n    preprocessing:\n      - id: fixed_zero_mean_unit_variance\n        kwargs:\n          axis: channel\n          mean: [101.5, 102.5, 103.5]\n          std: [11.7, 12.7, 13.7]\n    ```\n    - in Python\n    >>> preprocessing = [FixedZeroMeanUnitVarianceDescr(\n    ...   kwargs=FixedZeroMeanUnitVarianceAlongAxisKwargs(\n    ...     axis=AxisId(\"channel\"),\n    ...     mean=[101.5, 102.5, 103.5],\n    ...     std=[11.7, 12.7, 13.7],\n    ...   )\n    ... )]","properties":{"id":{"const":"fixed_zero_mean_unit_variance","title":"Id","type":"string"},"kwargs":{"anyOf":[{"$ref":"#/$defs/FixedZeroMeanUnitVarianceKwargs"},{"$ref":"#/$defs/FixedZeroMeanUnitVarianceAlongAxisKwargs"}],"title":"Kwargs"}},"required":["id","kwargs"],"title":"model.v0_5.FixedZeroMeanUnitVarianceDescr","type":"object"},"FixedZeroMeanUnitVarianceKwargs":{"additionalProperties":false,"description":"key word arguments for `FixedZeroMeanUnitVarianceDescr`","properties":{"mean":{"description":"The 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The axis and the referenced axis need to have the same unit (or no unit).\n2. Batch axes may not be referenced.\n3. Fractions are rounded down.\n4. 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Please use an image smaller than 500KB and an aspect ratio width to height of 2:1.\nThe supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg', '.tif', '.tiff')","examples":[["cover.png"]],"items":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"}]},"title":"Covers","type":"array"},"id_emoji":{"anyOf":[{"examples":["\ud83e\udd88","\ud83e\udda5"],"maxLength":1,"minLength":1,"type":"string"},{"type":"null"}],"default":null,"description":"UTF-8 emoji for display alongside the `id`.","title":"Id Emoji"},"authors":{"description":"The authors are the creators of the RDF and the primary points of contact.","items":{"$ref":"#/$defs/bioimageio__spec__generic__v0_2__Author"},"title":"Authors","type":"array"},"attachments":{"anyOf":[{"$ref":"#/$defs/AttachmentsDescr"},{"type":"null"}],"default":null,"description":"file and other attachments"},"cite":{"description":"citations","items":{"$ref":"#/$defs/bioimageio__spec__generic__v0_2__CiteEntry"},"title":"Cite","type":"array"},"config":{"additionalProperties":{"$ref":"#/$defs/YamlValue"},"description":"A field for custom configuration that can contain any keys not present in the RDF spec.\nThis means you should not store, for example, a github repo URL in `config` since we already have the\n`git_repo` field defined in the spec.\nKeys in `config` may be very specific to a tool or consumer software. To avoid conflicting definitions,\nit is recommended to wrap added configuration into a sub-field named with the specific domain or tool name,\nfor example:\n```yaml\nconfig:\n    bioimageio:  # here is the domain name\n        my_custom_key: 3837283\n        another_key:\n            nested: value\n    imagej:       # config specific to ImageJ\n        macro_dir: path/to/macro/file\n```\nIf possible, please use [`snake_case`](https://en.wikipedia.org/wiki/Snake_case) for keys in `config`.\nYou may want to list linked files additionally under `attachments` to include them when packaging a resource\n(packaging a resource means downloading/copying important linked files and creating a ZIP archive that contains\nan altered rdf.yaml file with local references to the downloaded files)","examples":[{"bioimageio":{"another_key":{"nested":"value"},"my_custom_key":3837283},"imagej":{"macro_dir":"path/to/macro/file"}}],"title":"Config","type":"object"},"download_url":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"type":"null"}],"default":null,"description":"URL to download the resource from (deprecated)","title":"Download Url"},"git_repo":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"A URL to the Git repository where the resource is being developed.","examples":["https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad"],"title":"Git Repo"},"icon":{"anyOf":[{"maxLength":2,"minLength":1,"type":"string"},{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"An icon for illustration","title":"Icon"},"links":{"description":"IDs of other bioimage.io resources","examples":[["ilastik/ilastik","deepimagej/deepimagej","zero/notebook_u-net_3d_zerocostdl4mic"]],"items":{"type":"string"},"title":"Links","type":"array"},"uploader":{"anyOf":[{"$ref":"#/$defs/Uploader"},{"type":"null"}],"default":null,"description":"The person who uploaded the model (e.g. to bioimage.io)"},"maintainers":{"description":"Maintainers of this resource.\nIf not specified `authors` are maintainers and at least some of them should specify their `github_user` name","items":{"$ref":"#/$defs/bioimageio__spec__generic__v0_2__Maintainer"},"title":"Maintainers","type":"array"},"rdf_source":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.\nDo not set this field in a YAML file.","title":"Rdf Source"},"tags":{"description":"Associated tags","examples":[["unet2d","pytorch","nucleus","segmentation","dsb2018"]],"items":{"type":"string"},"title":"Tags","type":"array"},"version":{"anyOf":[{"$ref":"#/$defs/Version"},{"type":"null"}],"default":null,"description":"The version of the resource following SemVer 2.0."},"version_number":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"version number (n-th published version, not the semantic version)","title":"Version Number"},"format_version":{"const":"0.2.4","description":"The format version of this resource specification\n(not the `version` of the resource description)\nWhen creating a new resource always use the latest micro/patch version described here.\nThe `format_version` is important for any consumer software to understand how to parse the fields.","title":"Format Version","type":"string"},"badges":{"description":"badges associated with this resource","items":{"$ref":"#/$defs/BadgeDescr"},"title":"Badges","type":"array"},"documentation":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"URL or relative path to a markdown file with additional documentation.\nThe recommended documentation file name is `README.md`. 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[SPDX license identifier](https://spdx.org/licenses/).\nWe do not support custom license beyond the SPDX license list, if you need that please\n[open a GitHub issue](https://github.com/bioimage-io/spec-bioimage-io/issues/new/choose)\nto discuss your intentions with the community.","examples":["CC0-1.0","MIT","BSD-2-Clause"],"title":"License"},"git_repo":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"type":"null"}],"default":null,"description":"A URL to the Git repository where the resource is being developed.","examples":["https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad"],"title":"Git Repo"},"icon":{"anyOf":[{"maxLength":2,"minLength":1,"type":"string"},{"description":"A URL with the HTTP or HTTPS 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To avoid conflicting definitions,\nit is recommended to wrap added configuration into a sub-field named with the specific domain or tool name,\nfor example:\n```yaml\nconfig:\n    bioimageio:  # here is the domain name\n        my_custom_key: 3837283\n        another_key:\n            nested: value\n    imagej:       # config specific to ImageJ\n        macro_dir: path/to/macro/file\n```\nIf possible, please use [`snake_case`](https://en.wikipedia.org/wiki/Snake_case) for keys in `config`.\nYou may want to list linked files additionally under `attachments` to include them when packaging a resource\n(packaging a resource means downloading/copying important linked files and creating a ZIP archive that contains\nan altered rdf.yaml file with local references to the downloaded files)","examples":[{"bioimageio":{"another_key":{"nested":"value"},"my_custom_key":3837283},"imagej":{"macro_dir":"path/to/macro/file"}}],"title":"Config","type":"object"},"download_url":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"type":"null"}],"default":null,"description":"URL to download the resource from (deprecated)","title":"Download Url"},"git_repo":{"anyOf":[{"type":"string"},{"type":"null"}],"default":null,"description":"A URL to the Git repository where the resource is being developed.","examples":["https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad"],"title":"Git Repo"},"icon":{"anyOf":[{"maxLength":2,"minLength":1,"type":"string"},{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"An icon for illustration","title":"Icon"},"links":{"description":"IDs of other bioimage.io resources","examples":[["ilastik/ilastik","deepimagej/deepimagej","zero/notebook_u-net_3d_zerocostdl4mic"]],"items":{"type":"string"},"title":"Links","type":"array"},"uploader":{"anyOf":[{"$ref":"#/$defs/Uploader"},{"type":"null"}],"default":null,"description":"The person who uploaded the model (e.g. to bioimage.io)"},"maintainers":{"description":"Maintainers of this resource.\nIf not specified `authors` are maintainers and at least some of them should specify their `github_user` name","items":{"$ref":"#/$defs/bioimageio__spec__generic__v0_2__Maintainer"},"title":"Maintainers","type":"array"},"rdf_source":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.\nDo not set this field in a YAML file.","title":"Rdf Source"},"tags":{"description":"Associated tags","examples":[["unet2d","pytorch","nucleus","segmentation","dsb2018"]],"items":{"type":"string"},"title":"Tags","type":"array"},"version":{"anyOf":[{"$ref":"#/$defs/Version"},{"type":"null"}],"default":null,"description":"The version of the resource following SemVer 2.0."},"version_number":{"anyOf":[{"type":"integer"},{"type":"null"}],"default":null,"description":"version number (n-th published version, not the semantic version)","title":"Version Number"},"format_version":{"const":"0.2.4","description":"The format version of this resource specification\n(not the `version` of the resource description)\nWhen creating a new resource always use the latest micro/patch version described here.\nThe `format_version` is important for any consumer software to understand how to parse the fields.","title":"Format Version","type":"string"},"badges":{"description":"badges associated with this resource","items":{"$ref":"#/$defs/BadgeDescr"},"title":"Badges","type":"array"},"documentation":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"$ref":"#/$defs/RelativeFilePath"},{"format":"file-path","title":"FilePath","type":"string"},{"type":"null"}],"default":null,"description":"URL or relative path to a markdown file with additional documentation.\nThe recommended documentation file name is `README.md`. An `.md` suffix is mandatory.","examples":["https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md","README.md"],"title":"Documentation"},"license":{"anyOf":[{"enum":["0BSD","AAL","Abstyles","AdaCore-doc","Adobe-2006","Adobe-Display-PostScript","Adobe-Glyph","Adobe-Utopia","ADSL","AFL-1.1","AFL-1.2","AFL-2.0","AFL-2.1","AFL-3.0","Afmparse","AGPL-1.0-only","AGPL-1.0-or-later","AGPL-3.0-only","AGPL-3.0-or-later","Aladdin","AMDPLPA","AML","AML-glslang","AMPAS","ANTLR-PD","ANTLR-PD-fallback","Apache-1.0","Apache-1.1","Apache-2.0","APAFML","APL-1.0","App-s2p","APSL-1.0","APSL-1.1","APSL-1.2","APSL-2.0","Arphic-1999","Artistic-1.0","Artistic-1.0-cl8","Artistic-1.0-Perl","Artistic-2.0","ASWF-Digital-Assets-1.0","ASWF-Digital-Assets-1.1","Baekmuk","Bahyph","Barr","bcrypt-Solar-Designer","Beerware","Bitstream-Charter","Bitstream-Vera","BitTorrent-1.0","BitTorrent-1.1","blessing","BlueOak-1.0.0","Boehm-GC","Borceux","Brian-Gladman-2-Clause","Brian-Gladman-3-Clause","BSD-1-Clause","BSD-2-Clause","BSD-2-Clause-Darwin","BSD-2-Clause-Patent","BSD-2-Clause-Views","BSD-3-Clause","BSD-3-Clause-acpica","BSD-3-Clause-Attribution","BSD-3-Clause-Clear","BSD-3-Clause-flex","BSD-3-Clause-HP","BSD-3-Clause-LBNL","BSD-3-Clause-Modification","BSD-3-Clause-No-Military-License","BSD-3-Clause-No-Nuclear-License","BSD-3-Clause-No-Nuclear-License-2014","BSD-3-Clause-No-Nuclear-Warranty","BSD-3-Clause-Open-MPI","BSD-3-Clause-Sun","BSD-4-Clause","BSD-4-Clause-Shortened","BSD-4-Clause-UC","BSD-4.3RENO","BSD-4.3TAHOE","BSD-Advertising-Acknowledgement","BSD-Attribution-HPND-disclaimer","BSD-Inferno-Nettverk","BSD-Protection","BSD-Source-beginning-file","BSD-Source-Code","BSD-Systemics","BSD-Systemics-W3Works","BSL-1.0","BUSL-1.1","bzip2-1.0.6","C-UDA-1.0","CAL-1.0","CAL-1.0-Combined-Work-Exception","Caldera","Caldera-no-preamble","CATOSL-1.1","CC-BY-1.0","CC-BY-2.0","CC-BY-2.5","CC-BY-2.5-AU","CC-BY-3.0","CC-BY-3.0-AT","CC-BY-3.0-AU","CC-BY-3.0-DE","CC-BY-3.0-IGO","CC-BY-3.0-NL","CC-BY-3.0-US","CC-BY-4.0","CC-BY-NC-1.0","CC-BY-NC-2.0","CC-BY-NC-2.5","CC-BY-NC-3.0","CC-BY-NC-3.0-DE","CC-BY-NC-4.0","CC-BY-NC-ND-1.0","CC-BY-NC-ND-2.0","CC-BY-NC-ND-2.5","CC-BY-NC-ND-3.0","CC-BY-NC-ND-3.0-DE","CC-BY-NC-ND-3.0-IGO","CC-BY-NC-ND-4.0","CC-BY-NC-SA-1.0","CC-BY-NC-SA-2.0","CC-BY-NC-SA-2.0-DE","CC-BY-NC-SA-2.0-FR","CC-BY-NC-SA-2.0-UK","CC-BY-NC-SA-2.5","CC-BY-NC-SA-3.0","CC-BY-NC-SA-3.0-DE","CC-BY-NC-SA-3.0-IGO","CC-BY-NC-SA-4.0","CC-BY-ND-1.0","CC-BY-ND-2.0","CC-BY-ND-2.5","CC-BY-ND-3.0","CC-BY-ND-3.0-DE","CC-BY-ND-4.0","CC-BY-SA-1.0","CC-BY-SA-2.0","CC-BY-SA-2.0-UK","CC-BY-SA-2.1-JP","CC-BY-SA-2.5","CC-BY-SA-3.0","CC-BY-SA-3.0-AT","CC-BY-SA-3.0-DE","CC-BY-SA-3.0-IGO","CC-BY-SA-4.0","CC-PDDC","CC0-1.0","CDDL-1.0","CDDL-1.1","CDL-1.0","CDLA-Permissive-1.0","CDLA-Permissive-2.0","CDLA-Sharing-1.0","CECILL-1.0","CECILL-1.1","CECILL-2.0","CECILL-2.1","CECILL-B","CECILL-C","CERN-OHL-1.1","CERN-OHL-1.2","CERN-OHL-P-2.0","CERN-OHL-S-2.0","CERN-OHL-W-2.0","CFITSIO","check-cvs","checkmk","ClArtistic","Clips","CMU-Mach","CMU-Mach-nodoc","CNRI-Jython","CNRI-Python","CNRI-Python-GPL-Compatible","COIL-1.0","Community-Spec-1.0","Condor-1.1","copyleft-next-0.3.0","copyleft-next-0.3.1","Cornell-Lossless-JPEG","CPAL-1.0","CPL-1.0","CPOL-1.02","Cronyx","Crossword","CrystalStacker","CUA-OPL-1.0","Cube","curl","D-FSL-1.0","DEC-3-Clause","diffmark","DL-DE-BY-2.0","DL-DE-ZERO-2.0","DOC","Dotseqn","DRL-1.0","DRL-1.1","DSDP","dtoa","dvipdfm","ECL-1.0","ECL-2.0","EFL-1.0","EFL-2.0","eGenix","Elastic-2.0","Entessa","EPICS","EPL-1.0","EPL-2.0","ErlPL-1.1","etalab-2.0","EUDatagrid","EUPL-1.0","EUPL-1.1","EUPL-1.2","Eurosym","Fair","FBM","FDK-AAC","Ferguson-Twofish","Frameworx-1.0","FreeBSD-DOC","FreeImage","FSFAP","FSFAP-no-warranty-disclaimer","FSFUL","FSFULLR","FSFULLRWD","FTL","Furuseth","fwlw","GCR-docs","GD","GFDL-1.1-invariants-only","GFDL-1.1-invariants-or-later","GFDL-1.1-no-invariants-only","GFDL-1.1-no-invariants-or-later","GFDL-1.1-only","GFDL-1.1-or-later","GFDL-1.2-invariants-only","GFDL-1.2-invariants-or-later","GFDL-1.2-no-invariants-only","GFDL-1.2-no-invariants-or-later","GFDL-1.2-only","GFDL-1.2-or-later","GFDL-1.3-invariants-only","GFDL-1.3-invariants-or-later","GFDL-1.3-no-invariants-only","GFDL-1.3-no-invariants-or-later","GFDL-1.3-only","GFDL-1.3-or-later","Giftware","GL2PS","Glide","Glulxe","GLWTPL","gnuplot","GPL-1.0-only","GPL-1.0-or-later","GPL-2.0-only","GPL-2.0-or-later","GPL-3.0-only","GPL-3.0-or-later","Graphics-Gems","gSOAP-1.3b","gtkbook","HaskellReport","hdparm","Hippocratic-2.1","HP-1986","HP-1989","HPND","HPND-DEC","HPND-doc","HPND-doc-sell","HPND-export-US","HPND-export-US-modify","HPND-Fenneberg-Livingston","HPND-INRIA-IMAG","HPND-Kevlin-Henney","HPND-Markus-Kuhn","HPND-MIT-disclaimer","HPND-Pbmplus","HPND-sell-MIT-disclaimer-xserver","HPND-sell-regexpr","HPND-sell-variant","HPND-sell-variant-MIT-disclaimer","HPND-UC","HTMLTIDY","IBM-pibs","ICU","IEC-Code-Components-EULA","IJG","IJG-short","I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[SPDX license identifier](https://spdx.org/licenses/).\nWe do not support custom license beyond the SPDX license list, if you need that please\n[open a GitHub issue](https://github.com/bioimage-io/spec-bioimage-io/issues/new/choose\n) to discuss your intentions with the community.","examples":["CC0-1.0","MIT","BSD-2-Clause"],"title":"License"},"type":{"const":"dataset","title":"Type","type":"string"},"id":{"anyOf":[{"minLength":1,"title":"DatasetId","type":"string"},{"type":"null"}],"default":null,"description":"bioimage.io-wide unique resource identifier\nassigned by bioimage.io; version **un**specific.","title":"Id"},"source":{"anyOf":[{"description":"A URL with the HTTP or HTTPS scheme.","format":"uri","maxLength":2083,"minLength":1,"title":"HttpUrl","type":"string"},{"type":"null"}],"default":null,"description":"\"URL to the source of the dataset.","title":"Source"}},"required":["name","description","format_version","type"],"title":"dataset 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</schema>

[User] Provide a JSON object that satisfies the schema "bioimageio_schema_latest". Here are the required top‑level fields: none (all optional). Return only the valid JSON object.

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Architecture source file

Source

A SHA-256 hash value

Sha256

SHA256 hash value of the **source** file.

Sha256

Identifier of the callable that returns a torch.nn.Module instance.

Identifier

key word arguments for the `callable`

Kwargs

model.v0_5.ArchitectureFromFileDescr

Identifier

key word arguments for the `callable`

Kwargs

Where to import the callable from, i.e. `from <import_from> import <callable>`

Import From

model.v0_5.ArchitectureFromLibraryDescr

File attachments

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Files

generic.v0_2.AttachmentsDescr

A custom badge

badge label to display on hover

Label

FilePath

A URL with the HTTP or HTTPS scheme.

HttpUrl

badge icon (included in bioimage.io package if not a URL)

Icon

target URL

HttpUrl

generic.v0_2.BadgeDescr

AxisId

Description

Type

The batch size may be fixed to 1,
otherwise (the default) it may be chosen arbitrarily depending on available memory

Size

model.v0_5.BatchAxis

key word arguments for `BinarizeDescr`

The fixed threshold values along `axis`

Threshold

The `threshold` axis

AxisId

model.v0_5.BinarizeAlongAxisKwargs

AxisId

Description

Type

Identifier

Channel Names

model.v0_5.ChannelAxis

key word arguments for `ClipDescr`

minimum value for clipping

Min

maximum value for clipping

Max

model.v0_4.ClipKwargs

Min

Max

model.v0_5.DataDependentSize

Timestamp in [ISO 8601](#https://en.wikipedia.org/wiki/ISO_8601) format
with a few restrictions listed [here](https://docs.python.org/3/library/datetime.html#datetime.datetime.fromisoformat).

Datetime

Cast the tensor data type to `EnsureDtypeKwargs.dtype` (if not matching).

This can for example be used to ensure the inner neural network model gets a
different input tensor data type than the fully described bioimage.io model does.

Examples:
    The described bioimage.io model (incl. preprocessing) accepts any
    float32-compatible tensor, normalizes it with percentiles and clipping and then
    casts it to uint8, which is what the neural network in this example expects.
    - in YAML
        ```yaml
        inputs:
        - data:
            type: float32  # described bioimage.io model is compatible with any float32 input tensor
          preprocessing:
          - id: scale_range
              kwargs:
              axes: ['y', 'x']
              max_percentile: 99.8
              min_percentile: 5.0
          - id: clip
              kwargs:
              min: 0.0
              max: 1.0
          - id: ensure_dtype  # the neural network of the model requires uint8
              kwargs:
              dtype: uint8
        ```
    - in Python:
        >>> preprocessing = [
        ...     ScaleRangeDescr(
        ...         kwargs=ScaleRangeKwargs(
        ...           axes= (AxisId('y'), AxisId('x')),
        ...           max_percentile= 99.8,
        ...           min_percentile= 5.0,
        ...         )
        ...     ),
        ...     ClipDescr(kwargs=ClipKwargs(min=0.0, max=1.0)),
        ...     EnsureDtypeDescr(kwargs=EnsureDtypeKwargs(dtype="uint8")),
        ... ]

Id

model.v0_5.EnsureDtypeDescr

key word arguments for `EnsureDtypeDescr`

Dtype

model.v0_5.EnsureDtypeKwargs

A file description

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

File source

Source

A SHA-256 hash value

Sha256

Sha256

_internal.io.FileDescr

key word arguments for `FixedZeroMeanUnitVarianceDescr`

The mean value(s) to normalize with.

Mean

The standard deviation value(s) to normalize with.
Size must match `mean` values.

Std

The axis of the mean/std values to normalize each entry along that dimension
separately.

AxisId

model.v0_5.FixedZeroMeanUnitVarianceAlongAxisKwargs

Subtract a given mean and divide by the standard deviation.

Normalize with fixed, precomputed values for
`FixedZeroMeanUnitVarianceKwargs.mean` and `FixedZeroMeanUnitVarianceKwargs.std`
Use `FixedZeroMeanUnitVarianceAlongAxisKwargs` for independent scaling along given
axes.

Examples:
1. scalar value for whole tensor
    - in YAML
    ```yaml
    preprocessing:
      - id: fixed_zero_mean_unit_variance
        kwargs:
          mean: 103.5
          std: 13.7
    ```
    - in Python
    >>> preprocessing = [FixedZeroMeanUnitVarianceDescr(
    ...   kwargs=FixedZeroMeanUnitVarianceKwargs(mean=103.5, std=13.7)
    ... )]

2. independently along an axis
    - in YAML
    ```yaml
    preprocessing:
      - id: fixed_zero_mean_unit_variance
        kwargs:
          axis: channel
          mean: [101.5, 102.5, 103.5]
          std: [11.7, 12.7, 13.7]
    ```
    - in Python
    >>> preprocessing = [FixedZeroMeanUnitVarianceDescr(
    ...   kwargs=FixedZeroMeanUnitVarianceAlongAxisKwargs(
    ...     axis=AxisId("channel"),
    ...     mean=[101.5, 102.5, 103.5],
    ...     std=[11.7, 12.7, 13.7],
    ...   )
    ... )]

Id

Kwargs

model.v0_5.FixedZeroMeanUnitVarianceDescr

The mean value to normalize with.

Mean

The standard deviation value to normalize with.

Std

model.v0_5.FixedZeroMeanUnitVarianceKwargs

Output tensor shape depending on an input tensor shape.
`shape(output_tensor) = shape(input_tensor) * scale + 2 * offset`

Name of the reference tensor.

TensorName

output_pix/input_pix for each dimension.
'null' values indicate new dimensions, whose length is defined by 2*`offset`

Scale

Position of origin wrt to input.

Offset

model.v0_4.ImplicitOutputShape

The size/length of this axis can be specified as
- fixed integer
- parameterized series of valid sizes (`ParameterizedSize`)
- reference to another axis with an optional offset (`SizeReference`)

Size

AxisId

Description

Type

If a model has a `concatenable` input axis, it can be processed blockwise,
splitting a longer sample axis into blocks matching its input tensor description.
Output axes are concatenable if they have a `SizeReference` to a concatenable
input axis.

Concatenable

model.v0_5.IndexInputAxis

AxisId

Description

Type

The size/length of this axis can be specified as
- fixed integer
- reference to another axis with an optional offset (`SizeReference`)
- data dependent size using `DataDependentSize` (size is only known after model inference)

Size

model.v0_5.IndexOutputAxis

Type

Tuple `(minimum, maximum)` specifying the allowed range of the data in this tensor.
`None` corresponds to min/max of what can be expressed by **type**.

Range

An SI unit

SiUnit

Unit

Scale for data on an interval (or ratio) scale.

Scale

Offset for data on a ratio scale.

Offset

model.v0_5.IntervalOrRatioDataDescr

A fixed set of nominal or an ascending sequence of ordinal values.
In this case `data.type` is required to be an unsigend integer type, e.g. 'uint8'.
String `values` are interpreted as labels for tensor values 0, ..., N.
Note: as YAML 1.2 does not natively support a "set" datatype,
nominal values should be given as a sequence (aka list/array) as well.

Values

Type

An SI unit

SiUnit

Unit

model.v0_5.NominalOrOrdinalDataDescr

A sequence of valid shapes given by `shape_k = min + k * step for k in {0, 1, ...}`.

The minimum input shape

Min

The minimum shape change

Step

model.v0_4.ParameterizedInputShape

Describes a range of valid tensor axis sizes as `size = min + n*step`.

- **min** and **step** are given by the model description.
- All blocksize paramters n = 0,1,2,... yield a valid `size`.
- A greater blocksize paramter n = 0,1,2,... results in a greater **size**.
  This allows to adjust the axis size more generically.

Min

Step

model.v0_5.ParameterizedSize

A path relative to the `rdf.yaml` file (also if the RDF source is a URL).

RelativeFilePath

Describes what small numerical differences -- if any -- may be tolerated
in the generated output when executing in different environments.

A tensor element *output* is considered mismatched to the **test_tensor** if
abs(*output* - **test_tensor**) > **absolute_tolerance** + **relative_tolerance** * abs(**test_tensor**).
(Internally we call [numpy.testing.assert_allclose](https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_allclose.html).)

Motivation:
    For testing we can request the respective deep learning frameworks to be as
    reproducible as possible by setting seeds and chosing deterministic algorithms,
    but differences in operating systems, available hardware and installed drivers
    may still lead to numerical differences.

Maximum relative tolerance of reproduced test tensor.

Relative Tolerance

Maximum absolute tolerance of reproduced test tensor.

Absolute Tolerance

Maximum number of mismatched elements/pixels per million to tolerate.

Mismatched Elements Per Million

Limits the output tensor IDs these reproducibility details apply to.

TensorId

Output Ids

Limits the weights formats these details apply to.

Weights Formats

model.v0_5.ReproducibilityTolerance

Run mode name

Name

Run mode specific key word arguments

Kwargs

model.v0_4.RunMode

Key word arguments for `ScaleLinearDescr`

The axis of gain and offset values.

AxisId

multiplicative factor

Gain

additive term

Offset

model.v0_5.ScaleLinearAlongAxisKwargs

A tensor axis size (extent in pixels/frames) defined in relation to a reference axis.

`axis.size = reference.size * reference.scale / axis.scale + offset`

Note:
1. The axis and the referenced axis need to have the same unit (or no unit).
2. Batch axes may not be referenced.
3. Fractions are rounded down.
4. If the reference axis is `concatenable` the referencing axis is assumed to be
    `concatenable` as well with the same block order.

Example:
An unisotropic input image of w*h=100*49 pixels depicts a phsical space of 200*196mm².
Let's assume that we want to express the image height h in relation to its width w
instead of only accepting input images of exactly 100*49 pixels
(for example to express a range of valid image shapes by parametrizing w, see `ParameterizedSize`).

>>> w = SpaceInputAxis(id=AxisId("w"), size=100, unit="millimeter", scale=2)
>>> h = SpaceInputAxis(
...     id=AxisId("h"),
...     size=SizeReference(tensor_id=TensorId("input"), axis_id=AxisId("w"), offset=-1),
...     unit="millimeter",
...     scale=4,
... )
>>> print(h.size.get_size(h, w))
49

⇒ h = w * w.scale / h.scale + offset = 100 * 2mm / 4mm - 1 = 49

tensor id of the reference axis

TensorId

axis id of the reference axis

AxisId

Offset

model.v0_5.SizeReference

The size/length of this axis can be specified as
- fixed integer
- parameterized series of valid sizes (`ParameterizedSize`)
- reference to another axis with an optional offset (`SizeReference`)

Size

AxisId

Description

Type

attometer angstrom centimeter decimeter exameter femtometer foot gigameter hectometer inch kilometer megameter meter micrometer mile millimeter nanometer parsec petameter picometer terameter yard yoctometer yottameter zeptometer zettameter

Unit

Scale

Concatenable

model.v0_5.SpaceInputAxis

The size/length of this axis can be specified as
- fixed integer
- reference to another axis with an optional offset (see `SizeReference`)

Size

AxisId

Description

Type

Unit

Scale

model.v0_5.SpaceOutputAxis

The halo should be cropped from the output tensor to avoid boundary effects.
It is to be cropped from both sides, i.e. `size_after_crop = size - 2 * halo`.
To document a halo that is already cropped by the model use `size.offset` instead.

Halo

reference to another axis with an optional offset (see `SizeReference`)

AxisId

Description

Type

Unit

Scale

model.v0_5.SpaceOutputAxisWithHalo

Size

AxisId

Description

Type

attosecond centisecond day decisecond exasecond femtosecond gigasecond hectosecond hour kilosecond megasecond microsecond millisecond minute nanosecond petasecond picosecond second terasecond yoctosecond yottasecond zeptosecond zettasecond

Unit

Scale

Concatenable

model.v0_5.TimeInputAxis

Size

AxisId

Description

Type

Unit

Scale

model.v0_5.TimeOutputAxis

Halo

AxisId

Description

Type

Unit

Scale

model.v0_5.TimeOutputAxisWithHalo

Email

Email

name

Name

generic.v0_2.Uploader

wraps a packaging.version.Version instance for validation in pydantic models

Version

Bioimage.io description of an application.

A human-friendly name of the resource description

Name

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg', '.tif', '.tiff')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of the RDF and the primary points of contact.

Authors

file and other attachments

citations

Cite

A field for custom configuration that can contain any keys not present in the RDF spec.
This means you should not store, for example, a github repo URL in `config` since we already have the
`git_repo` field defined in the spec.
Keys in `config` may be very specific to a tool or consumer software. To avoid conflicting definitions,
it is recommended to wrap added configuration into a sub-field named with the specific domain or tool name,
for example:
```yaml
config:
    bioimageio:  # here is the domain name
        my_custom_key: 3837283
        another_key:
            nested: value
    imagej:       # config specific to ImageJ
        macro_dir: path/to/macro/file
```
If possible, please use [`snake_case`](https://en.wikipedia.org/wiki/Snake_case) for keys in `config`.
You may want to list linked files additionally under `attachments` to include them when packaging a resource
(packaging a resource means downloading/copying important linked files and creating a ZIP archive that contains
an altered rdf.yaml file with local references to the downloaded files)

Config

A URL with the HTTP or HTTPS scheme.

HttpUrl

URL to download the resource from (deprecated)

Download Url

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified `authors` are maintainers and at least some of them should specify their `github_user` name

Maintainers

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.
Do not set this field in a YAML file.

Rdf Source

Associated tags

Tags

The version of the resource following SemVer 2.0.

version number (n-th published version, not the semantic version)

Version Number

The format version of this resource specification
(not the `version` of the resource description)
When creating a new resource always use the latest micro/patch version described here.
The `format_version` is important for any consumer software to understand how to parse the fields.

Format Version

badges associated with this resource

Badges

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

LicenseId

DeprecatedLicenseId

A [SPDX license identifier](https://spdx.org/licenses/).
We do not support custom license beyond the SPDX license list, if you need that please
[open a GitHub issue](https://github.com/bioimage-io/spec-bioimage-io/issues/new/choose
) to discuss your intentions with the community.

License

Type

ApplicationId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or path to the source of the application

Source

application 0.2.4

Bioimage.io description of an application.

A human-friendly name of the resource description.
May only contains letters, digits, underscore, minus, parentheses and spaces.

Name

A string containing a brief description.

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1 or 1:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of this resource description and the primary points of contact.

Authors

file attachments

Attachments

citations

Cite

LicenseId

DeprecatedLicenseId

A [SPDX license identifier](https://spdx.org/licenses/).
We do not support custom license beyond the SPDX license list, if you need that please
[open a GitHub issue](https://github.com/bioimage-io/spec-bioimage-io/issues/new/choose)
to discuss your intentions with the community.

License

A URL with the HTTP or HTTPS scheme.

HttpUrl

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration, e.g. on bioimage.io

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified, `authors` are maintainers and at least some of them has to specify their `github_user` name

Maintainers

Associated tags

Tags

The version of the resource following SemVer 2.0.

The **format** version of this resource specification

Format Version

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file encoded in UTF-8 with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

badges associated with this resource

Badges

A field for custom configuration that can contain any keys not present in the RDF spec.
This means you should not store, for example, a GitHub repo URL in `config` since there is a `git_repo` field.
Keys in `config` may be very specific to a tool or consumer software. To avoid conflicting definitions,
it is recommended to wrap added configuration into a sub-field named with the specific domain or tool name,
for example:
```yaml
config:
    giraffe_neckometer:  # here is the domain name
        length: 3837283
        address:
            home: zoo
    imagej:              # config specific to ImageJ
        macro_dir: path/to/macro/file
```
If possible, please use [`snake_case`](https://en.wikipedia.org/wiki/Snake_case) for keys in `config`.
You may want to list linked files additionally under `attachments` to include them when packaging a resource.
(Packaging a resource means downloading/copying important linked files and creating a ZIP archive that contains
an altered rdf.yaml file with local references to the downloaded files.)

Type

ApplicationId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

ApplicationId

The description from which this one is derived

Parent

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or path to the source of the application

Source

name description authors cite license format_version type

application 0.3.0

A bioimage.io dataset resource description file (dataset RDF) describes a dataset relevant to bioimage
processing.

A human-friendly name of the resource description

Name

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg', '.tif', '.tiff')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of the RDF and the primary points of contact.

Authors

file and other attachments

citations

Cite

Config

A URL with the HTTP or HTTPS scheme.

HttpUrl

URL to download the resource from (deprecated)

Download Url

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified `authors` are maintainers and at least some of them should specify their `github_user` name

Maintainers

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.
Do not set this field in a YAML file.

Rdf Source

Associated tags

Tags

The version of the resource following SemVer 2.0.

version number (n-th published version, not the semantic version)

Version Number

Format Version

badges associated with this resource

Badges

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

LicenseId

DeprecatedLicenseId

License

Type

DatasetId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

A URL with the HTTP or HTTPS scheme.

HttpUrl

"URL to the source of the dataset.

Source

dataset 0.2.4

Reference to a bioimage.io dataset.

A valid dataset `id` from the bioimage.io collection.

DatasetId

version number (n-th published version, not the semantic version) of linked dataset

Version Number

dataset.v0_2.LinkedDataset

A bioimage.io dataset resource description file (dataset RDF) describes a dataset relevant to bioimage
processing.

A human-friendly name of the resource description.
May only contains letters, digits, underscore, minus, parentheses and spaces.

Name

A string containing a brief description.

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1 or 1:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of this resource description and the primary points of contact.

Authors

file attachments

Attachments

citations

Cite

LicenseId

DeprecatedLicenseId

License

A URL with the HTTP or HTTPS scheme.

HttpUrl

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration, e.g. on bioimage.io

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified, `authors` are maintainers and at least some of them has to specify their `github_user` name

Maintainers

Associated tags

Tags

The version of the resource following SemVer 2.0.

The **format** version of this resource specification

Format Version

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file encoded in UTF-8 with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

badges associated with this resource

Badges

Type

DatasetId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

DatasetId

The description from which this one is derived

Parent

A URL with the HTTP or HTTPS scheme.

HttpUrl

"URL to the source of the dataset.

Source

dataset 0.3.0

Reference to a bioimage.io dataset.

The version of the linked resource following SemVer 2.0.

A valid dataset `id` from the bioimage.io collection.

DatasetId

dataset.v0_3.LinkedDataset

Affiliation

Affiliation

Email

Email

An ORCID identifier, see https://orcid.org/

OrcidId

An [ORCID iD](https://support.orcid.org/hc/en-us/sections/360001495313-What-is-ORCID
) in hyphenated groups of 4 digits, (and [valid](
https://support.orcid.org/hc/en-us/articles/360006897674-Structure-of-the-ORCID-Identifier
) as per ISO 7064 11,2.)

Orcid

Name

Github User

generic.v0_2.Author

free text description

Text

A digital object identifier, see https://www.doi.org/

Doi

A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify `url`)

Doi

URL to cite (preferably specify a `doi` instead)

Url

generic.v0_2.CiteEntry

Affiliation

Affiliation

Email

Email

OrcidId

Orcid

Name

Github User

generic.v0_2.Maintainer

Affiliation

Affiliation

Email

Email

OrcidId

Orcid

Name

Github User

generic.v0_3.Author

bioimage.io internal metadata.

generic.v0_3.BioimageioConfig

A citation that should be referenced in work using this resource.

free text description

Text

Doi

A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details.
Note:
    Either **doi** or **url** have to be specified.

Doi

A URL with the HTTP or HTTPS scheme.

HttpUrl

URL to cite (preferably specify a **doi** instead/also).
Note:
    Either **doi** or **url** have to be specified.

Url

generic.v0_3.CiteEntry

A place to store additional metadata (often tool specific).

Such additional metadata is typically set programmatically by the respective tool
or by people with specific insights into the tool.
If you want to store additional metadata that does not match any of the other
fields, think of a key unlikely to collide with anyone elses use-case/tool and save
it here.

Please consider creating [an issue in the bioimageio.spec repository](https://github.com/bioimage-io/spec-bioimage-io/issues/new?template=Blank+issue)
if you are not sure if an existing field could cover your use case
or if you think such a field should exist.

generic.v0_3.Config

Affiliation

Affiliation

Email

Email

OrcidId

Orcid

Name

Github User

generic.v0_3.Maintainer

BinarizeDescr the tensor with a fixed `BinarizeKwargs.threshold`.
Values above the threshold will be set to one, values below the threshold to zero.

Name

model.v0_4.BinarizeDescr

key word arguments for `BinarizeDescr`

The fixed threshold

Threshold

model.v0_4.BinarizeKwargs

Clip tensor values to a range.

Set tensor values below `ClipKwargs.min` to `ClipKwargs.min`
and above `ClipKwargs.max` to `ClipKwargs.max`.

Name

model.v0_4.ClipDescr

Tensor name. No duplicates are allowed.

TensorName

Description

Axes identifying characters. Same length and order as the axes in `shape`.
| axis | description |
| --- | --- |
|  b  |  batch (groups multiple samples) |
|  i  |  instance/index/element |
|  t  |  time |
|  c  |  channel |
|  z  |  spatial dimension z |
|  y  |  spatial dimension y |
|  x  |  spatial dimension x |

Axes

Tuple `(minimum, maximum)` specifying the allowed range of the data in this tensor.
If not specified, the full data range that can be expressed in `data_type` is allowed.

Data Range

For now an input tensor is expected to be given as `float32`.
The data flow in bioimage.io models is explained
[in this diagram.](https://docs.google.com/drawings/d/1FTw8-Rn6a6nXdkZ_SkMumtcjvur9mtIhRqLwnKqZNHM/edit).

Data Type

Specification of input tensor shape.

Shape

Description of how this input should be preprocessed.

Preprocessing

model.v0_4.InputTensorDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The weights file.

Source

A SHA-256 hash value

Sha256

SHA256 hash value of the **source** file.

Sha256

Attachments that are specific to this weights entry.

Authors
Either the person(s) that have trained this model resulting in the original weights file.
    (If this is the initial weights entry, i.e. it does not have a `parent`)
Or the person(s) who have converted the weights to this weights format.
    (If this is a child weight, i.e. it has a `parent` field)

Authors

Dependencies

Dependency manager and dependency file, specified as `<dependency manager>:<relative file path>`.

Dependencies

The source weights these weights were converted from.
For example, if a model's weights were converted from the `pytorch_state_dict` format to `torchscript`,
The `pytorch_state_dict` weights entry has no `parent` and is the parent of the `torchscript` weights.
All weight entries except one (the initial set of weights resulting from training the model),
need to have this field.

Parent

TensorFlow version used to create these weights

Reference to a bioimage.io model.

A valid model `id` from the bioimage.io collection.

ModelId

version number (n-th published version, not the semantic version) of linked model

Version Number

model.v0_4.LinkedModel

Specification of the fields used in a bioimage.io-compliant RDF that describes AI models with pretrained weights.

These fields are typically stored in a YAML file which we call a model resource description file (model RDF).

A human-readable name of this model.
It should be no longer than 64 characters and only contain letter, number, underscore, minus or space characters.

Name

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg', '.tif', '.tiff')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of the model RDF and the primary points of contact.

Authors

file and other attachments

citations

Cite

Config

A URL with the HTTP or HTTPS scheme.

HttpUrl

URL to download the resource from (deprecated)

Download Url

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified `authors` are maintainers and at least some of them should specify their `github_user` name

Maintainers

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.
Do not set this field in a YAML file.

Rdf Source

Associated tags

Tags

The version of the resource following SemVer 2.0.

version number (n-th published version, not the semantic version)

Version Number

Version of the bioimage.io model description specification used.
When creating a new model always use the latest micro/patch version described here.
The `format_version` is important for any consumer software to understand how to parse the fields.

Format Version

Specialized resource type 'model'

Type

ModelId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.
The documentation should include a '[#[#]]# Validation' (sub)section
with details on how to quantitatively validate the model on unseen data.

Documentation

Describes the input tensors expected by this model.

Inputs

LicenseId

A [SPDX license identifier](https://spdx.org/licenses/).
We do notsupport custom license beyond the SPDX license list, if you need that please
[open a GitHub issue](https://github.com/bioimage-io/spec-bioimage-io/issues/new/choose
) to discuss your intentions with the community.

License

Describes the output tensors.

Outputs

The persons that have packaged and uploaded this model.
Only required if those persons differ from the `authors`.

Packaged By

The model from which this model is derived, e.g. by fine-tuning the weights.

Custom run mode for this model: for more complex prediction procedures like test time
data augmentation that currently cannot be expressed in the specification.
No standard run modes are defined yet.

URLs/relative paths to sample inputs to illustrate possible inputs for the model,
for example stored as PNG or TIFF images.
The sample files primarily serve to inform a human user about an example use case

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Sample Inputs

URLs/relative paths to sample outputs corresponding to the `sample_inputs`.

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Sample Outputs

Test input tensors compatible with the `inputs` description for a **single test case**.
This means if your model has more than one input, you should provide one URL/relative path for each input.
Each test input should be a file with an ndarray in
[numpy.lib file format](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format).
The extension must be '.npy'.

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Test Inputs

Analog to `test_inputs`.

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Test Outputs

The dataset used to train this model

Training Data

The weights for this model.
Weights can be given for different formats, but should otherwise be equivalent.
The available weight formats determine which consumers can use this model.

name description authors format_version type documentation inputs license outputs test_inputs test_outputs timestamp weights

model 0.4.10

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Dependencies

Dependencies

Parent

ONNX opset version

Opset Version

model.v0_4.OnnxWeightsDescr

Tensor name. No duplicates are allowed.

TensorName

Description

Axes

Data Range

Data type.
The data flow in bioimage.io models is explained
[in this diagram.](https://docs.google.com/drawings/d/1FTw8-Rn6a6nXdkZ_SkMumtcjvur9mtIhRqLwnKqZNHM/edit).

Data Type

Output tensor shape.

Shape

The `halo` that should be cropped from the output tensor to avoid boundary effects.
The `halo` is to be cropped from both sides, i.e. `shape_after_crop = shape - 2 * halo`.
To document a `halo` that is already cropped by the model `shape.offset` has to be used instead.

Halo

Description of how this output should be postprocessed.

Postprocessing

model.v0_4.OutputTensorDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Dependencies

Dependencies

Parent

CallableFromFile

CallableFromDepencency

callable returning a torch.nn.Module instance.
Local implementation: `<relative path to file>:<identifier of implementation within the file>`.
Implementation in a dependency: `<dependency-package>.<[dependency-module]>.<identifier>`.

Architecture

A SHA-256 hash value

Sha256

The SHA256 of the architecture source file, if the architecture is not defined in a module listed in `dependencies`
You can drag and drop your file to this
[online tool](http://emn178.github.io/online-tools/sha256_checksum.html) to generate a SHA256 in your browser.
Or you can generate a SHA256 checksum with Python's `hashlib`,
[here is a codesnippet](https://gist.github.com/FynnBe/e64460463df89439cff218bbf59c1100).

Architecture Sha256

key word arguments for the `architecture` callable

Kwargs

Version of the PyTorch library used.
If `depencencies` is specified it should include pytorch and the verison has to match.
(`dependencies` overrules `pytorch_version`)

model.v0_4.PytorchStateDictWeightsDescr

Fixed linear scaling.

Name

model.v0_4.ScaleLinearDescr

key word arguments for `ScaleLinearDescr`

The subset of axes to scale jointly.
For example xy to scale the two image axes for 2d data jointly.

Axes

multiplicative factor

Gain

additive term

Offset

model.v0_4.ScaleLinearKwargs

Scale the tensor s.t. its mean and variance match a reference tensor.

Name

model.v0_4.ScaleMeanVarianceDescr

key word arguments for `ScaleMeanVarianceDescr`

Mode for computing mean and variance.
|     mode    |             description              |
| ----------- | ------------------------------------ |
| per_dataset | Compute for the entire dataset       |
| per_sample  | Compute for each sample individually |

Mode

Name of tensor to match.

TensorName

The subset of axes to scale jointly.
For example xy to normalize the two image axes for 2d data jointly.
Default: scale all non-batch axes jointly.

Axes

Epsilon for numeric stability:
"`out  = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.

Eps

model.v0_4.ScaleMeanVarianceKwargs

Scale with percentiles.

Name

model.v0_4.ScaleRangeDescr

key word arguments for `ScaleRangeDescr`

For `min_percentile`=0.0 (the default) and `max_percentile`=100 (the default)
this processing step normalizes data to the [0, 1] intervall.
For other percentiles the normalized values will partially be outside the [0, 1]
intervall. Use `ScaleRange` followed by `ClipDescr` if you want to limit the
normalized values to a range.

Mode for computing percentiles.
|     mode    |             description              |
| ----------- | ------------------------------------ |
| per_dataset | compute for the entire dataset       |
| per_sample  | compute for each sample individually |

Mode

The subset of axes to normalize jointly.
For example xy to normalize the two image axes for 2d data jointly.

Axes

The lower percentile used to determine the value to align with zero.

Min Percentile

The upper percentile used to determine the value to align with one.
Has to be bigger than `min_percentile`.
The range is 1 to 100 instead of 0 to 100 to avoid mistakenly
accepting percentiles specified in the range 0.0 to 1.0.

Max Percentile

Epsilon for numeric stability.
`out = (tensor - v_lower) / (v_upper - v_lower + eps)`;
with `v_lower,v_upper` values at the respective percentiles.

Eps

TensorName

Tensor name to compute the percentiles from. Default: The tensor itself.
For any tensor in `inputs` only input tensor references are allowed.
For a tensor in `outputs` only input tensor refereences are allowed if `mode: per_dataset`

Reference Tensor

model.v0_4.ScaleRangeKwargs

The logistic sigmoid funciton, a.k.a. expit function.

Name

model.v0_4.SigmoidDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The multi-file weights.
All required files/folders should be a zip archive.

Source

A SHA-256 hash value

Sha256

SHA256 hash value of the **source** file.

Sha256

Attachments that are specific to this weights entry.

Authors

Dependencies

Dependency manager and dependency file, specified as `<dependency manager>:<relative file path>`.

Dependencies

Parent

Version of the TensorFlow library used.

model.v0_4.TensorflowJsWeightsDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Dependencies

Dependencies

Parent

Version of the TensorFlow library used.

model.v0_4.TensorflowSavedModelBundleWeightsDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Dependencies

Dependencies

Parent

Version of the PyTorch library used.

model.v0_4.TorchscriptWeightsDescr

model.v0_4.WeightsDescr

Subtract mean and divide by variance.

Name

model.v0_4.ZeroMeanUnitVarianceDescr

key word arguments for `ZeroMeanUnitVarianceDescr`

Mode for computing mean and variance.
|     mode    |             description              |
| ----------- | ------------------------------------ |
|   fixed     | Fixed values for mean and variance   |
| per_dataset | Compute for the entire dataset       |
| per_sample  | Compute for each sample individually |

Mode

The subset of axes to normalize jointly.
For example `xy` to normalize the two image axes for 2d data jointly.

Axes

The mean value(s) to use for `mode: fixed`.
For example `[1.1, 2.2, 3.3]` in the case of a 3 channel image with `axes: xy`.

Mean

The standard deviation values to use for `mode: fixed`. Analogous to mean.

Std

epsilon for numeric stability: `out = (tensor - mean) / (std + eps)`.

Eps

model.v0_4.ZeroMeanUnitVarianceKwargs

Binarize the tensor with a fixed threshold.

Values above `BinarizeKwargs.threshold`/`BinarizeAlongAxisKwargs.threshold`
will be set to one, values below the threshold to zero.

Examples:
- in YAML
    ```yaml
    postprocessing:
      - id: binarize
        kwargs:
          axis: 'channel'
          threshold: [0.25, 0.5, 0.75]
    ```
- in Python:
    >>> postprocessing = [BinarizeDescr(
    ...   kwargs=BinarizeAlongAxisKwargs(
    ...       axis=AxisId('channel'),
    ...       threshold=[0.25, 0.5, 0.75],
    ...   )
    ... )]

Id

Kwargs

model.v0_5.BinarizeDescr

key word arguments for `BinarizeDescr`

The fixed threshold

Threshold

model.v0_5.BinarizeKwargs

Tolerances to allow when reproducing the model's test outputs
from the model's test inputs.
Only the first entry matching tensor id and weights format is considered.

Reproducibility Tolerance

model.v0_5.BioimageioConfig

Set tensor values below min to min and above max to max.

See `ScaleRangeDescr` for examples.

Id

model.v0_5.ClipDescr

model.v0_5.Config

Input tensor id.
No duplicates are allowed across all inputs and outputs.

TensorId

free text description

Description

tensor axes

Axes

An example tensor to use for testing.
Using the model with the test input tensors is expected to yield the test output tensors.
Each test tensor has be a an ndarray in the
[numpy.lib file format](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format).
The file extension must be '.npy'.

A sample tensor to illustrate a possible input/output for the model,
The sample image primarily serves to inform a human user about an example use case
and is typically stored as .hdf5, .png or .tiff.
It has to be readable by the [imageio library](https://imageio.readthedocs.io/en/stable/formats/index.html#supported-formats)
(numpy's `.npy` format is not supported).
The image dimensionality has to match the number of axes specified in this tensor description.

Description of the tensor's data values, optionally per channel.
If specified per channel, the data `type` needs to match across channels.

Data

indicates that this tensor may be `None`

Optional

Description of how this input should be preprocessed.

notes:
- If preprocessing does not start with an 'ensure_dtype' entry, it is added
  to ensure an input tensor's data type matches the input tensor's data description.
- If preprocessing does not end with an 'ensure_dtype' or 'binarize' entry, an
  'ensure_dtype' step is added to ensure preprocessing steps are not unintentionally
  changing the data type.

Preprocessing

model.v0_5.InputTensorDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Source of the weights file.

Source

A SHA-256 hash value

Sha256

SHA256 hash value of the **source** file.

Sha256

Authors

Parent

A comment about this weights entry, for example how these weights were created.

Comment

TensorFlow version used to create these weights.

Reference to a bioimage.io model.

The version of the linked resource following SemVer 2.0.

A valid model `id` from the bioimage.io collection.

ModelId

model.v0_5.LinkedModel

Specification of the fields used in a bioimage.io-compliant RDF to describe AI models with pretrained weights.
These fields are typically stored in a YAML file which we call a model resource description file (model RDF).

A human-readable name of this model.
It should be no longer than 64 characters
and may only contain letter, number, underscore, minus, parentheses and spaces.
We recommend to chose a name that refers to the model's task and image modality.

Name

A string containing a brief description.

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1 or 1:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of the model RDF and the primary points of contact.

Authors

file attachments

Attachments

citations

Cite

LicenseId

DeprecatedLicenseId

License

A URL with the HTTP or HTTPS scheme.

HttpUrl

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration, e.g. on bioimage.io

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified, `authors` are maintainers and at least some of them has to specify their `github_user` name

Maintainers

Associated tags

Tags

The version of the resource following SemVer 2.0.

Format Version

Specialized resource type 'model'

Type

ModelId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.
The documentation should include a '#[#] Validation' (sub)section
with details on how to quantitatively validate the model on unseen data.

Documentation

Describes the input tensors expected by this model.

Inputs

Describes the output tensors.

Outputs

The persons that have packaged and uploaded this model.
Only required if those persons differ from the `authors`.

Packaged By

The model from which this model is derived, e.g. by fine-tuning the weights.

Custom run mode for this model: for more complex prediction procedures like test time
data augmentation that currently cannot be expressed in the specification.
No standard run modes are defined yet.

Timestamp in [ISO 8601](#https://en.wikipedia.org/wiki/ISO_8601) format
with a few restrictions listed [here](https://docs.python.org/3/library/datetime.html#datetime.datetime.fromisoformat).
(In Python a datetime object is valid, too).

The dataset used to train this model

Training Data

The weights for this model.
Weights can be given for different formats, but should otherwise be equivalent.
The available weight formats determine which consumers can use this model.

name description authors cite license format_version type documentation inputs outputs weights

model 0.5.4

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Source of the weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Parent

Comment

ONNX opset version

Opset Version

model.v0_5.OnnxWeightsDescr

Output tensor id.
No duplicates are allowed across all inputs and outputs.

TensorId

free text description

Description

tensor axes

Axes

Data

Description of how this output should be postprocessed.

note: `postprocessing` always ends with an 'ensure_dtype' operation.
      If not given this is added to cast to this tensor's `data.type`.

Postprocessing

model.v0_5.OutputTensorDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Source of the weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Parent

Comment

Architecture

Version of the PyTorch library used.
If `architecture.depencencies` is specified it has to include pytorch and any version pinning has to be compatible.

Custom depencies beyond pytorch described in a Conda environment file.
Allows to specify custom dependencies, see conda docs:
- [Exporting an environment file across platforms](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#exporting-an-environment-file-across-platforms)
- [Creating an environment file manually](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-file-manually)

The conda environment file should include pytorch and any version pinning has to be compatible with
**pytorch_version**.

model.v0_5.PytorchStateDictWeightsDescr

Fixed linear scaling.

Examples:
  1. Scale with scalar gain and offset
    - in YAML
    ```yaml
    preprocessing:
      - id: scale_linear
        kwargs:
          gain: 2.0
          offset: 3.0
    ```
    - in Python:
    >>> preprocessing = [
    ...     ScaleLinearDescr(kwargs=ScaleLinearKwargs(gain= 2.0, offset=3.0))
    ... ]

  2. Independent scaling along an axis
    - in YAML
    ```yaml
    preprocessing:
      - id: scale_linear
        kwargs:
          axis: 'channel'
          gain: [1.0, 2.0, 3.0]
    ```
    - in Python:
    >>> preprocessing = [
    ...     ScaleLinearDescr(
    ...         kwargs=ScaleLinearAlongAxisKwargs(
    ...             axis=AxisId("channel"),
    ...             gain=[1.0, 2.0, 3.0],
    ...         )
    ...     )
    ... ]

Id

Kwargs

model.v0_5.ScaleLinearDescr

Key word arguments for `ScaleLinearDescr`

multiplicative factor

Gain

additive term

Offset

model.v0_5.ScaleLinearKwargs

Scale a tensor's data distribution to match another tensor's mean/std.
`out  = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.`

Id

model.v0_5.ScaleMeanVarianceDescr

key word arguments for `ScaleMeanVarianceKwargs`

Name of tensor to match.

TensorId

AxisId

The subset of axes to normalize jointly, i.e. axes to reduce to compute mean/std.
For example to normalize 'batch', 'x' and 'y' jointly in a tensor ('batch', 'channel', 'y', 'x')
resulting in a tensor of equal shape normalized per channel, specify `axes=('batch', 'x', 'y')`.
To normalize samples independently, leave out the 'batch' axis.
Default: Scale all axes jointly.

Axes

Epsilon for numeric stability:
`out  = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.`

Eps

model.v0_5.ScaleMeanVarianceKwargs

Scale with percentiles.

Examples:
1. Scale linearly to map 5th percentile to 0 and 99.8th percentile to 1.0
    - in YAML
    ```yaml
    preprocessing:
      - id: scale_range
        kwargs:
          axes: ['y', 'x']
          max_percentile: 99.8
          min_percentile: 5.0
    ```
    - in Python
    >>> preprocessing = [
    ...     ScaleRangeDescr(
    ...         kwargs=ScaleRangeKwargs(
    ...           axes= (AxisId('y'), AxisId('x')),
    ...           max_percentile= 99.8,
    ...           min_percentile= 5.0,
    ...         )
    ...     ),
    ...     ClipDescr(
    ...         kwargs=ClipKwargs(
    ...             min=0.0,
    ...             max=1.0,
    ...         )
    ...     ),
    ... ]

  2. Combine the above scaling with additional clipping to clip values outside the range given by the percentiles.
    - in YAML
    ```yaml
    preprocessing:
      - id: scale_range
        kwargs:
          axes: ['y', 'x']
          max_percentile: 99.8
          min_percentile: 5.0
              - id: scale_range
       - id: clip
         kwargs:
          min: 0.0
          max: 1.0
    ```
    - in Python
    >>> preprocessing = [ScaleRangeDescr(
    ...   kwargs=ScaleRangeKwargs(
    ...       axes= (AxisId('y'), AxisId('x')),
    ...       max_percentile= 99.8,
    ...       min_percentile= 5.0,
    ...   )
    ... )]

Id

model.v0_5.ScaleRangeDescr

AxisId

The subset of axes to normalize jointly, i.e. axes to reduce to compute the min/max percentile value.
For example to normalize 'batch', 'x' and 'y' jointly in a tensor ('batch', 'channel', 'y', 'x')
resulting in a tensor of equal shape normalized per channel, specify `axes=('batch', 'x', 'y')`.
To normalize samples independently, leave out the "batch" axis.
Default: Scale all axes jointly.

Axes

The lower percentile used to determine the value to align with zero.

Min Percentile

The upper percentile used to determine the value to align with one.
Has to be bigger than `min_percentile`.
The range is 1 to 100 instead of 0 to 100 to avoid mistakenly
accepting percentiles specified in the range 0.0 to 1.0.

Max Percentile

Epsilon for numeric stability.
`out = (tensor - v_lower) / (v_upper - v_lower + eps)`;
with `v_lower,v_upper` values at the respective percentiles.

Eps

TensorId

Tensor ID to compute the percentiles from. Default: The tensor itself.
For any tensor in `inputs` only input tensor references are allowed.

Reference Tensor

model.v0_5.ScaleRangeKwargs

The logistic sigmoid funciton, a.k.a. expit function.

Examples:
- in YAML
    ```yaml
    postprocessing:
      - id: sigmoid
    ```
- in Python:
    >>> postprocessing = [SigmoidDescr()]

Id

model.v0_5.SigmoidDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The multi-file weights.
All required files/folders should be a zip archive.

Source

A SHA-256 hash value

Sha256

SHA256 hash value of the **source** file.

Sha256

Authors

Parent

A comment about this weights entry, for example how these weights were created.

Comment

Version of the TensorFlow library used.

model.v0_5.TensorflowJsWeightsDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Parent

Comment

Version of the TensorFlow library used.

Custom dependencies beyond tensorflow.
Should include tensorflow and any version pinning has to be compatible with **tensorflow_version**.

model.v0_5.TensorflowSavedModelBundleWeightsDescr

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Source of the weights file.

Source

A SHA-256 hash value

Sha256

Sha256

Authors

Parent

Comment

Version of the PyTorch library used.

model.v0_5.TorchscriptWeightsDescr

model.v0_5.WeightsDescr

Subtract mean and divide by variance.

Examples:
    Subtract tensor mean and variance
    - in YAML
    ```yaml
    preprocessing:
      - id: zero_mean_unit_variance
    ```
    - in Python
    >>> preprocessing = [ZeroMeanUnitVarianceDescr()]

Id

model.v0_5.ZeroMeanUnitVarianceDescr

key word arguments for `ZeroMeanUnitVarianceDescr`

AxisId

The subset of axes to normalize jointly, i.e. axes to reduce to compute mean/std.
For example to normalize 'batch', 'x' and 'y' jointly in a tensor ('batch', 'channel', 'y', 'x')
resulting in a tensor of equal shape normalized per channel, specify `axes=('batch', 'x', 'y')`.
To normalize each sample independently leave out the 'batch' axis.
Default: Scale all axes jointly.

Axes

epsilon for numeric stability: `out = (tensor - mean) / (std + eps)`.

Eps

model.v0_5.ZeroMeanUnitVarianceKwargs

Bioimage.io description of a Jupyter Notebook.

A human-friendly name of the resource description

Name

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg', '.tif', '.tiff')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of the RDF and the primary points of contact.

Authors

file and other attachments

citations

Cite

Config

A URL with the HTTP or HTTPS scheme.

HttpUrl

URL to download the resource from (deprecated)

Download Url

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified `authors` are maintainers and at least some of them should specify their `github_user` name

Maintainers

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Resource description file (RDF) source; used to keep track of where an rdf.yaml was loaded from.
Do not set this field in a YAML file.

Rdf Source

Associated tags

Tags

The version of the resource following SemVer 2.0.

version number (n-th published version, not the semantic version)

Version Number

Format Version

badges associated with this resource

Badges

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

LicenseId

DeprecatedLicenseId

License

Type

NotebookId

bioimage.io-wide unique resource identifier
assigned by bioimage.io; version **un**specific.

Id

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The Jupyter notebook

Source

name description format_version type source

notebook 0.2.4

Bioimage.io description of a Jupyter notebook.

A human-friendly name of the resource description.
May only contains letters, digits, underscore, minus, parentheses and spaces.

Name

A string containing a brief description.

Description

Cover images. Please use an image smaller than 500KB and an aspect ratio width to height of 2:1 or 1:1.
The supported image formats are: ('.gif', '.jpeg', '.jpg', '.png', '.svg')

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

Covers

UTF-8 emoji for display alongside the `id`.

Id Emoji

The authors are the creators of this resource description and the primary points of contact.

Authors

file attachments

Attachments

citations

Cite

LicenseId

DeprecatedLicenseId

License

A URL with the HTTP or HTTPS scheme.

HttpUrl

A URL to the Git repository where the resource is being developed.

Git Repo

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

An icon for illustration, e.g. on bioimage.io

Icon

IDs of other bioimage.io resources

Links

The person who uploaded the model (e.g. to bioimage.io)

Maintainers of this resource.
If not specified, `authors` are maintainers and at least some of them has to specify their `github_user` name

Maintainers

Associated tags

Tags

The version of the resource following SemVer 2.0.

The **format** version of this resource specification

Format Version

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

URL or relative path to a markdown file encoded in UTF-8 with additional documentation.
The recommended documentation file name is `README.md`. An `.md` suffix is mandatory.

Documentation

badges associated with this resource

Badges

Type

NotebookId

Id

NotebookId

The description from which this one is derived

Parent

A URL with the HTTP or HTTPS scheme.

HttpUrl

FilePath

The Jupyter notebook

Source

name description authors cite license format_version type source

notebook 0.3.0

application 0.2

application 0.3

application

dataset 0.2

dataset 0.3

dataset

model 0.4

model 0.5

model

notebook 0.2

notebook 0.3

notebook

application 0.2

application 0.3

application

dataset 0.2

dataset 0.3

dataset

model 0.4

model 0.5

model

notebook 0.2

notebook 0.3

notebook

Provide a JSON object that satisfies the schema "bioimageio_schema_latest". Here are the required top‑level fields: none (all optional). Return only the valid JSON object.