Bioimage.io description of an application.
No Additional PropertiesA human-friendly name of the resource description
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.2.4"
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
∈📦 URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is README.md. An .md suffix is mandatory.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
"application"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
URL or path to the source of the application
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Bioimage.io description of an application.
No Additional PropertiesA human-friendly name of the resource description.
May only contains letters, digits, underscore, minus, parentheses and spaces.
Must be at least 1 characters long
Must be at most 128 characters long
A string containing a brief description.
Must be at most 1024 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 2 characters long
🦈
🦥
file attachments
No Additional Items∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
citations
Must contain a minimum of 1 items
A citation that should be referenced in work using this resource.
No Additional Propertiesfree text description
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.
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead/also).
Note:
Either doi or url have to be specified.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue
to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
A URL to the Git repository where the resource is being developed.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration, e.g. on bioimage.io
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified, authors are maintainers and at least some of them has to specify their github_user name
Affiliation
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
The format version of this resource specification
"0.3.0"
∈📦 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.
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
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:
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 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.)
bioimage.io internal metadata.
Additional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: object"application"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
The description from which this one is derived
Must be at least 1 characters long
URL or path to the source of the application
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A bioimage.io dataset resource description file (dataset RDF) describes a dataset relevant to bioimage
processing.
A human-friendly name of the resource description
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.2.4"
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
∈📦 URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is README.md. An .md suffix is mandatory.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
"dataset"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
"URL to the source of the dataset.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
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.
Must be at least 1 characters long
Must be at most 128 characters long
A string containing a brief description.
Must be at most 1024 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 2 characters long
🦈
🦥
file attachments
No Additional Items∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
citations
Must contain a minimum of 1 items
A citation that should be referenced in work using this resource.
No Additional Propertiesfree text description
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.
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead/also).
Note:
Either doi or url have to be specified.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue
to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
A URL to the Git repository where the resource is being developed.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration, e.g. on bioimage.io
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified, authors are maintainers and at least some of them has to specify their github_user name
Affiliation
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
The format version of this resource specification
"0.3.0"
∈📦 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.
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
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:
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 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.)
bioimage.io internal metadata.
Additional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: object"dataset"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
The description from which this one is derived
Must be at least 1 characters long
"URL to the source of the dataset.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
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).
No Additional PropertiesA human-readable name of this model.
It should be no longer than 64 characters and only contain letter, number, underscore, minus or space characters.
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.4.10"
Specialized resource type 'model'
"model"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
Describes the input tensors expected by this model.
Must contain a minimum of 1 items
Tensor name. No duplicates are allowed.
Must be at least 1 characters long
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
For now an input tensor is expected to be given as float32.
The data flow in bioimage.io models is explained
in this diagram..
Specification of input tensor shape.
A sequence of valid shapes given by shape_k = min + k * step for k in {0, 1, ...}.
The minimum input shape
Must contain a minimum of 1 items
The minimum shape change
Must contain a minimum of 1 items
[1, 512, 512, 1]
min:
- 1
- 64
- 64
- 1
step:
- 0
- 32
- 32
- 0
Description of how this input should be preprocessed.
No Additional ItemsBinarizeDescr the tensor with a fixed BinarizeKwargs.threshold.
Values above the threshold will be set to one, values below the threshold to zero.
"binarize"
key word arguments for BinarizeDescr
The fixed threshold
Clip tensor values to a range.
Set tensor values below ClipKwargs.min to ClipKwargs.min
and above ClipKwargs.max to ClipKwargs.max.
"clip"
key word arguments for ClipDescr
minimum value for clipping
maximum value for clipping
Fixed linear scaling.
No Additional Properties"scale_linear"
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.
xy
multiplicative factor
additive term
The logistic sigmoid funciton, a.k.a. expit function.
No Additional Properties"sigmoid"
Subtract mean and divide by variance.
No Additional Properties"zero_mean_unit_variance"
key word arguments for ZeroMeanUnitVarianceDescr
The subset of axes to normalize jointly.
For example xy to normalize the two image axes for 2d data jointly.
xy
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.
Must contain a minimum of 1 items
[1.1, 2.2, 3.3]
The standard deviation values to use for mode: fixed. Analogous to mean.
Must contain a minimum of 1 items
[0.1, 0.2, 0.3]
epsilon for numeric stability: out = (tensor - mean) / (std + eps).
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Scale with percentiles.
No Additional Properties"scale_range"
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.
The subset of axes to normalize jointly.
For example xy to normalize the two image axes for 2d data jointly.
xy
The lower percentile used to determine the value to align with zero.
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.
Epsilon for numeric stability.
out = (tensor - v_lower) / (v_upper - v_lower + eps);
with v_lower,v_upper values at the respective percentiles.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
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
Must be at least 1 characters long
A SPDX license identifier.
We do notsupport custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
Describes the output tensors.
Must contain a minimum of 1 items
Tensor name. No duplicates are allowed.
Must be at least 1 characters long
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
Data type.
The data flow in bioimage.io models is explained
in this diagram..
Output tensor shape.
Output tensor shape depending on an input tensor shape.
shape(output_tensor) = shape(input_tensor) * scale + 2 * offset
Name of the reference tensor.
Must be at least 1 characters long
outputpix/inputpix for each dimension.
'null' values indicate new dimensions, whose length is defined by 2*offset
Must contain a minimum of 1 items
Position of origin wrt to input.
Must contain a minimum of 1 items
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.
Description of how this output should be postprocessed.
No Additional ItemsBinarizeDescr the tensor with a fixed BinarizeKwargs.threshold.
Values above the threshold will be set to one, values below the threshold to zero.
"binarize"
key word arguments for BinarizeDescr
The fixed threshold
Clip tensor values to a range.
Set tensor values below ClipKwargs.min to ClipKwargs.min
and above ClipKwargs.max to ClipKwargs.max.
"clip"
key word arguments for ClipDescr
minimum value for clipping
maximum value for clipping
Fixed linear scaling.
No Additional Properties"scale_linear"
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.
xy
multiplicative factor
additive term
The logistic sigmoid funciton, a.k.a. expit function.
No Additional Properties"sigmoid"
Subtract mean and divide by variance.
No Additional Properties"zero_mean_unit_variance"
key word arguments for ZeroMeanUnitVarianceDescr
The subset of axes to normalize jointly.
For example xy to normalize the two image axes for 2d data jointly.
xy
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.
Must contain a minimum of 1 items
[1.1, 2.2, 3.3]
The standard deviation values to use for mode: fixed. Analogous to mean.
Must contain a minimum of 1 items
[0.1, 0.2, 0.3]
epsilon for numeric stability: out = (tensor - mean) / (std + eps).
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Scale with percentiles.
No Additional Properties"scale_range"
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.
The subset of axes to normalize jointly.
For example xy to normalize the two image axes for 2d data jointly.
xy
The lower percentile used to determine the value to align with zero.
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.
Epsilon for numeric stability.
out = (tensor - v_lower) / (v_upper - v_lower + eps);
with v_lower,v_upper values at the respective percentiles.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
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
Must be at least 1 characters long
Scale the tensor s.t. its mean and variance match a reference tensor.
No Additional Properties"scale_mean_variance"
key word arguments for ScaleMeanVarianceDescr
Name of tensor to match.
Must be at least 1 characters long
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.
xy
Epsilon for numeric stability:
"`out = (tensor - mean) / (std + eps) * (refstd + eps) + refmean.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
The persons that have packaged and uploaded this model.
Only required if those persons differ from the authors.
Affiliation
The model from which this model is derived, e.g. by fine-tuning the weights.
Reference to a bioimage.io model.
No Additional PropertiesA valid model id from the bioimage.io collection.
Must be at least 1 characters long
affable-shark
ambitious-sloth
version number (n-th published version, not the semantic version) of linked model
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.
Run mode name
"deepimagej"
Run mode specific key word arguments
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.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
URLs/relative paths to sample outputs corresponding to the sample_inputs.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
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.
The extension must be '.npy'.
Must contain a minimum of 1 items
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Analog to test_inputs.
Must contain a minimum of 1 items
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The dataset used to train this model
Reference to a bioimage.io dataset.
No Additional PropertiesA valid dataset id from the bioimage.io collection.
Must be at least 1 characters long
version number (n-th published version, not the semantic version) of linked dataset
A bioimage.io dataset resource description file (dataset RDF) describes a dataset relevant to bioimage
processing.
A human-friendly name of the resource description
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.2.4"
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
∈📦 URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is README.md. An .md suffix is mandatory.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
"dataset"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
"URL to the source of the dataset.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
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.
The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
TensorFlow version used to create these weights
wraps a packaging.version.Version instance for validation in pydantic models
The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
ONNX opset version
Value must be greater or equal to 7
The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
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>.
^.+:.+$
^.+\..+$
my_function.py:MyNetworkClass
my_module.submodule.get_my_model
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 to generate a SHA256 in your browser.
Or you can generate a SHA256 checksum with Python's hashlib,
here is a codesnippet.
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
key word arguments for the architecture callable
Version of the PyTorch library used.
If depencencies is specified it should include pytorch and the verison has to match.
(dependencies overrules pytorch_version)
wraps a packaging.version.Version instance for validation in pydantic models
The multi-file weights.
All required files/folders should be a zip archive.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
Version of the TensorFlow library used.
wraps a packaging.version.Version instance for validation in pydantic models
The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
Version of the TensorFlow library used.
wraps a packaging.version.Version instance for validation in pydantic models
The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Attachments that are specific to this weights entry.
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectDependency manager and dependency file, specified as <dependency manager>:<relative file path>.
^.+:.+$
conda:environment.yaml
maven:./pom.xml
pip:./requirements.txt
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.
pytorch_state_dict
Version of the PyTorch library used.
wraps a packaging.version.Version instance for validation in pydantic models
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.
Must be at least 5 characters long
Must be at most 128 characters long
A string containing a brief description.
Must be at most 1024 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 2 characters long
🦈
🦥
file attachments
No Additional Items∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
citations
Must contain a minimum of 1 items
A citation that should be referenced in work using this resource.
No Additional Propertiesfree text description
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.
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead/also).
Note:
Either doi or url have to be specified.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue
to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
A URL to the Git repository where the resource is being developed.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration, e.g. on bioimage.io
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified, authors are maintainers and at least some of them has to specify their github_user name
Affiliation
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
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.
"0.5.4"
Specialized resource type 'model'
"model"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
∈📦 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.
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
Describes the input tensors expected by this model.
Must contain a minimum of 1 items
Input tensor id.
No duplicates are allowed across all inputs and outputs.
Must be at least 1 characters long
Must be at most 32 characters long
free text description
Must be at most 128 characters long
tensor axes
Must contain a minimum of 1 items
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"batch"
The batch size may be fixed to 1,
otherwise (the default) it may be chosen arbitrarily depending on available memory
1
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"channel"
Must contain a minimum of 1 items
Must be at least 1 characters long
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)
Value must be strictly greater than 0
Describes a range of valid tensor axis sizes as size = min + n*step.
size.Value must be strictly greater than 0
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
10
min: 32
step: 16
axis_id: a
offset: 5
tensor_id: t
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"index"
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.
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)
Value must be strictly greater than 0
Describes a range of valid tensor axis sizes as size = min + n*step.
size.Value must be strictly greater than 0
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
10
min: 32
step: 16
axis_id: a
offset: 5
tensor_id: t
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"time"
Value must be strictly greater than 0.0
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.
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)
Value must be strictly greater than 0
Describes a range of valid tensor axis sizes as size = min + n*step.
size.Value must be strictly greater than 0
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
10
min: 32
step: 16
axis_id: a
offset: 5
tensor_id: t
Must be at least 1 characters long
Must be at most 16 characters long
x
y
z
Must be at most 128 characters long
"space"
Value must be strictly greater than 0.0
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.
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.
The file extension must be '.npy'.
∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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
(numpy's .npy format is not supported).
The image dimensionality has to match the number of axes specified in this tensor description.
∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Description of the tensor's data values, optionally per channel.
If specified per channel, the data type needs to match across channels.
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.
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
float32
uint8
uint16
int64
bool
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
float32
float64
uint8
uint16
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
Scale for data on an interval (or ratio) scale.
Offset for data on a ratio scale.
Must contain a minimum of 1 items
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.
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
float32
uint8
uint16
int64
bool
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
float32
float64
uint8
uint16
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
Scale for data on an interval (or ratio) scale.
Offset for data on a ratio scale.
indicates that this tensor may be None
Description of how this input should be preprocessed.
notes:
- If preprocessing does not start with an 'ensuredtype' 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 'ensuredtype' or 'binarize' entry, an
'ensure_dtype' step is added to ensure preprocessing steps are not unintentionally
changing the data type.
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
postprocessing:
- id: binarize
kwargs:
axis: 'channel'
threshold: [0.25, 0.5, 0.75]
"binarize"
key word arguments for BinarizeDescr
The fixed threshold
key word arguments for BinarizeDescr
The fixed threshold values along axis
Must contain a minimum of 1 items
The threshold axis
Must be at least 1 characters long
Must be at most 16 characters long
channel
Set tensor values below min to min and above max to max.
See ScaleRangeDescr for examples.
"clip"
key word arguments for ClipDescr
minimum value for clipping
maximum value for clipping
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
inputs:
- data:
type: float32 # described bioimage.io model is compatible with any float32 input tensor
preprocessing:
- id: scalerange
kwargs:
axes: ['y', 'x']
maxpercentile: 99.8
minpercentile: 5.0
- id: clip
kwargs:
min: 0.0
max: 1.0
- id: ensuredtype
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")),
... ]
"ensure_dtype"
key word arguments for EnsureDtypeDescr
Fixed linear scaling.
Examples:
1. Scale with scalar gain and offset
- in YAML
preprocessing:
- id: scale_linear
kwargs:
gain: 2.0
offset: 3.0
- in Python:
>>> preprocessing = [
... ScaleLinearDescr(kwargs=ScaleLinearKwargs(gain= 2.0, offset=3.0))
... ]
Independent scaling along an axis
in 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],
... )
... )
... ]
"scale_linear"
Key word arguments for ScaleLinearDescr
multiplicative factor
additive term
Key word arguments for ScaleLinearDescr
The axis of gain and offset values.
Must be at least 1 characters long
Must be at most 16 characters long
channel
multiplicative factor
Must contain a minimum of 1 items
additive term
Must contain a minimum of 1 items
The logistic sigmoid funciton, a.k.a. expit function.
Examples:
- in YAML
postprocessing:
- id: sigmoid
"sigmoid"
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
preprocessing:
- id: fixedzeromeanunitvariance
kwargs:
mean: 103.5
std: 13.7
- in Python
>>> preprocessing = [FixedZeroMeanUnitVarianceDescr(
... kwargs=FixedZeroMeanUnitVarianceKwargs(mean=103.5, std=13.7)
... )]
independently along an axis
in 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],
... )
... )]
"fixed_zero_mean_unit_variance"
key word arguments for FixedZeroMeanUnitVarianceDescr
The mean value to normalize with.
The standard deviation value to normalize with.
Value must be greater or equal to 1e-06
key word arguments for FixedZeroMeanUnitVarianceDescr
The mean value(s) to normalize with.
Must contain a minimum of 1 items
The standard deviation value(s) to normalize with.
Size must match mean values.
Must contain a minimum of 1 items
Value must be greater or equal to 1e-06
The axis of the mean/std values to normalize each entry along that dimension
separately.
Must be at least 1 characters long
Must be at most 16 characters long
channel
index
Subtract mean and divide by variance.
Examples:
Subtract tensor mean and variance
- in YAML
preprocessing:
- id: zeromeanunit_variance
- in Python
>>> preprocessing = [ZeroMeanUnitVarianceDescr()]
"zero_mean_unit_variance"
key word arguments for ZeroMeanUnitVarianceDescr
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.
Must be at least 1 characters long
Must be at most 16 characters long
['batch', 'x', 'y']
epsilon for numeric stability: out = (tensor - mean) / (std + eps).
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Scale with percentiles.
Examples:
1. Scale linearly to map 5th percentile to 0 and 99.8th percentile to 1.0
- in YAML
preprocessing:
- id: scalerange
kwargs:
axes: ['y', 'x']
maxpercentile: 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,
... )
... ),
... ]
Combine the above scaling with additional clipping to clip values outside the range given by the percentiles.
in 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')),
... maxpercentile= 99.8,
... minpercentile= 5.0,
... )
... )]
"scale_range"
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.
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.
Must be at least 1 characters long
Must be at most 16 characters long
['batch', 'x', 'y']
The lower percentile used to determine the value to align with zero.
Value must be greater or equal to 0.0 and strictly lesser than 100.0
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.
Value must be strictly greater than 1.0 and lesser or equal to 100.0
Epsilon for numeric stability.
out = (tensor - v_lower) / (v_upper - v_lower + eps);
with v_lower,v_upper values at the respective percentiles.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Tensor ID to compute the percentiles from. Default: The tensor itself.
For any tensor in inputs only input tensor references are allowed.
Must be at least 1 characters long
Must be at most 32 characters long
Describes the output tensors.
Must contain a minimum of 1 items
Output tensor id.
No duplicates are allowed across all inputs and outputs.
Must be at least 1 characters long
Must be at most 32 characters long
free text description
Must be at most 128 characters long
tensor axes
Must contain a minimum of 1 items
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"batch"
The batch size may be fixed to 1,
otherwise (the default) it may be chosen arbitrarily depending on available memory
1
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"channel"
Must contain a minimum of 1 items
Must be at least 1 characters long
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"index"
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)
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
Value must be strictly greater than 0
Value must be strictly greater than 1
10
axis_id: a
offset: 5
tensor_id: t
The size/length of this axis can be specified as
- fixed integer
- reference to another axis with an optional offset (see SizeReference)
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
10
axis_id: a
offset: 5
tensor_id: t
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"time"
Value must be strictly greater than 0.0
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.
Value must be greater or equal to 1
reference to another axis with an optional offset (see SizeReference)
10
axis_id: a
offset: 5
tensor_id: t
tensor id of the reference axis
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
Must be at least 1 characters long
Must be at most 16 characters long
Must be at most 128 characters long
"time"
Value must be strictly greater than 0.0
The size/length of this axis can be specified as
- fixed integer
- reference to another axis with an optional offset (see SizeReference)
Value must be strictly greater than 0
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 wh=10049 pixels depicts a phsical space of 200196mm².
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 10049 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(tensorid=TensorId("input"), axisid=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
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
10
axis_id: a
offset: 5
tensor_id: t
Must be at least 1 characters long
Must be at most 16 characters long
x
y
z
Must be at most 128 characters long
"space"
Value must be strictly greater than 0.0
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.
Value must be greater or equal to 1
reference to another axis with an optional offset (see SizeReference)
10
axis_id: a
offset: 5
tensor_id: t
tensor id of the reference axis
Must be at least 1 characters long
Must be at most 32 characters long
axis id of the reference axis
Must be at least 1 characters long
Must be at most 16 characters long
Must be at least 1 characters long
Must be at most 16 characters long
x
y
z
Must be at most 128 characters long
"space"
Value must be strictly greater than 0.0
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.
The file extension must be '.npy'.
∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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
(numpy's .npy format is not supported).
The image dimensionality has to match the number of axes specified in this tensor description.
∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Description of the tensor's data values, optionally per channel.
If specified per channel, the data type needs to match across channels.
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.
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
float32
uint8
uint16
int64
bool
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
float32
float64
uint8
uint16
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
Scale for data on an interval (or ratio) scale.
Offset for data on a ratio scale.
Must contain a minimum of 1 items
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.
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
Must contain a minimum of 1 items
float32
uint8
uint16
int64
bool
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
float32
float64
uint8
uint16
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.
Must contain a minimum of 2 items
Must contain a maximum of 2 items
"arbitrary unit"
An SI unit
Must match regular expression:^(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?((·(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^[+-]?[1-9]\d*)?)|(/(Q|R|Y|Z|E|P|T|G|M|k|h|da|d|c|m|µ|n|p|f|a|z|y|r|q)?(m|g|s|A|K|mol|cd|Hz|N|Pa|J|W|C|V|F|Ω|S|Wb|T|H|lm|lx|Bq|Gy|Sv|kat|l|L)(\^+?[1-9]\d*)?))*$
Must be at least 1 characters long
Scale for data on an interval (or ratio) scale.
Offset for data on a ratio scale.
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.
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
postprocessing:
- id: binarize
kwargs:
axis: 'channel'
threshold: [0.25, 0.5, 0.75]
"binarize"
key word arguments for BinarizeDescr
The fixed threshold
key word arguments for BinarizeDescr
The fixed threshold values along axis
Must contain a minimum of 1 items
The threshold axis
Must be at least 1 characters long
Must be at most 16 characters long
channel
Set tensor values below min to min and above max to max.
See ScaleRangeDescr for examples.
"clip"
key word arguments for ClipDescr
minimum value for clipping
maximum value for clipping
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
inputs:
- data:
type: float32 # described bioimage.io model is compatible with any float32 input tensor
preprocessing:
- id: scalerange
kwargs:
axes: ['y', 'x']
maxpercentile: 99.8
minpercentile: 5.0
- id: clip
kwargs:
min: 0.0
max: 1.0
- id: ensuredtype
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")),
... ]
"ensure_dtype"
key word arguments for EnsureDtypeDescr
Fixed linear scaling.
Examples:
1. Scale with scalar gain and offset
- in YAML
preprocessing:
- id: scale_linear
kwargs:
gain: 2.0
offset: 3.0
- in Python:
>>> preprocessing = [
... ScaleLinearDescr(kwargs=ScaleLinearKwargs(gain= 2.0, offset=3.0))
... ]
Independent scaling along an axis
in 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],
... )
... )
... ]
"scale_linear"
Key word arguments for ScaleLinearDescr
multiplicative factor
additive term
Key word arguments for ScaleLinearDescr
The axis of gain and offset values.
Must be at least 1 characters long
Must be at most 16 characters long
channel
multiplicative factor
Must contain a minimum of 1 items
additive term
Must contain a minimum of 1 items
The logistic sigmoid funciton, a.k.a. expit function.
Examples:
- in YAML
postprocessing:
- id: sigmoid
"sigmoid"
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
preprocessing:
- id: fixedzeromeanunitvariance
kwargs:
mean: 103.5
std: 13.7
- in Python
>>> preprocessing = [FixedZeroMeanUnitVarianceDescr(
... kwargs=FixedZeroMeanUnitVarianceKwargs(mean=103.5, std=13.7)
... )]
independently along an axis
in 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],
... )
... )]
"fixed_zero_mean_unit_variance"
key word arguments for FixedZeroMeanUnitVarianceDescr
The mean value to normalize with.
The standard deviation value to normalize with.
Value must be greater or equal to 1e-06
key word arguments for FixedZeroMeanUnitVarianceDescr
The mean value(s) to normalize with.
Must contain a minimum of 1 items
The standard deviation value(s) to normalize with.
Size must match mean values.
Must contain a minimum of 1 items
Value must be greater or equal to 1e-06
The axis of the mean/std values to normalize each entry along that dimension
separately.
Must be at least 1 characters long
Must be at most 16 characters long
channel
index
Subtract mean and divide by variance.
Examples:
Subtract tensor mean and variance
- in YAML
preprocessing:
- id: zeromeanunit_variance
- in Python
>>> preprocessing = [ZeroMeanUnitVarianceDescr()]
"zero_mean_unit_variance"
key word arguments for ZeroMeanUnitVarianceDescr
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.
Must be at least 1 characters long
Must be at most 16 characters long
['batch', 'x', 'y']
epsilon for numeric stability: out = (tensor - mean) / (std + eps).
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Scale with percentiles.
Examples:
1. Scale linearly to map 5th percentile to 0 and 99.8th percentile to 1.0
- in YAML
preprocessing:
- id: scalerange
kwargs:
axes: ['y', 'x']
maxpercentile: 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,
... )
... ),
... ]
Combine the above scaling with additional clipping to clip values outside the range given by the percentiles.
in 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')),
... maxpercentile= 99.8,
... minpercentile= 5.0,
... )
... )]
"scale_range"
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.
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.
Must be at least 1 characters long
Must be at most 16 characters long
['batch', 'x', 'y']
The lower percentile used to determine the value to align with zero.
Value must be greater or equal to 0.0 and strictly lesser than 100.0
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.
Value must be strictly greater than 1.0 and lesser or equal to 100.0
Epsilon for numeric stability.
out = (tensor - v_lower) / (v_upper - v_lower + eps);
with v_lower,v_upper values at the respective percentiles.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
Tensor ID to compute the percentiles from. Default: The tensor itself.
For any tensor in inputs only input tensor references are allowed.
Must be at least 1 characters long
Must be at most 32 characters long
Scale a tensor's data distribution to match another tensor's mean/std.
out = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.
"scale_mean_variance"
key word arguments for ScaleMeanVarianceKwargs
Name of tensor to match.
Must be at least 1 characters long
Must be at most 32 characters long
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.
Must be at least 1 characters long
Must be at most 16 characters long
['batch', 'x', 'y']
Epsilon for numeric stability:
out = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.
Value must be strictly greater than 0.0 and lesser or equal to 0.1
The persons that have packaged and uploaded this model.
Only required if those persons differ from the authors.
Affiliation
The model from which this model is derived, e.g. by fine-tuning the weights.
Reference to a bioimage.io model.
No Additional PropertiesThe version of the linked resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
A valid model id from the bioimage.io collection.
Must be at least 1 characters long
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.
Run mode name
"deepimagej"
Run mode specific key word arguments
The dataset used to train this model
Reference to a bioimage.io dataset.
No Additional PropertiesThe version of the linked resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
A valid dataset id from the bioimage.io collection.
Must be at least 1 characters long
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.
Must be at least 1 characters long
Must be at most 128 characters long
A string containing a brief description.
Must be at most 1024 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 2 characters long
🦈
🦥
file attachments
No Additional Items∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
citations
Must contain a minimum of 1 items
A citation that should be referenced in work using this resource.
No Additional Propertiesfree text description
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.
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead/also).
Note:
Either doi or url have to be specified.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue
to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
A URL to the Git repository where the resource is being developed.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration, e.g. on bioimage.io
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified, authors are maintainers and at least some of them has to specify their github_user name
Affiliation
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
The format version of this resource specification
"0.3.0"
∈📦 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.
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
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:
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 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.)
bioimage.io internal metadata.
Additional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: object"dataset"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
The description from which this one is derived
Must be at least 1 characters long
"URL to the source of the dataset.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A bioimage.io dataset resource description file (dataset RDF) describes a dataset relevant to bioimage
processing.
A human-friendly name of the resource description
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.2.4"
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
∈📦 URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is README.md. An .md suffix is mandatory.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
"dataset"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
"URL to the source of the dataset.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
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.
∈📦 The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
TensorFlow version used to create these weights.
∈📦 The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
ONNX opset version
Value must be greater or equal to 7
∈📦 The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
Identifier of the callable that returns a torch.nn.Module instance.
Must be at least 1 characters long
MyNetworkClass
get_my_model
key word arguments for the callable
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectIdentifier of the callable that returns a torch.nn.Module instance.
Must be at least 1 characters long
MyNetworkClass
get_my_model
key word arguments for the callable
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectWhere to import the callable from, i.e. from <import_from> import <callable>
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.
The conda environment file should include pytorch and any version pinning has to be compatible with
pytorch_version.
∈📦 Conda environment file.
Allows to specify custom dependencies, see conda docs:
- Exporting an environment file across platforms
- Creating an environment file manually
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
environment.yaml
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
∈📦 The multi-file weights.
All required files/folders should be a zip archive.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
Version of the TensorFlow library used.
∈📦 The multi-file weights.
All required files/folders should be a zip archive.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
Version of the TensorFlow library used.
Custom dependencies beyond tensorflow.
Should include tensorflow and any version pinning has to be compatible with tensorflow_version.
∈📦 Conda environment file.
Allows to specify custom dependencies, see conda docs:
- Exporting an environment file across platforms
- Creating an environment file manually
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
environment.yaml
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
∈📦 The weights file.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
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.
pytorch_state_dict
A comment about this weights entry, for example how these weights were created.
Version of the PyTorch library used.
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.
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 testtensor if
abs(*output* - testtensor) > absolutetolerance + relativetolerance * abs(testtensor).
(Internally we call numpy.testing.assertallclose.)
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.
Value must be greater or equal to 0.0 and lesser or equal to 0.01
Maximum absolute tolerance of reproduced test tensor.
Value must be greater or equal to 0.0
Maximum number of mismatched elements/pixels per million to tolerate.
Value must be greater or equal to 0 and lesser or equal to 100
Limits the output tensor IDs these reproducibility details apply to.
No Additional ItemsMust be at least 1 characters long
Must be at most 32 characters long
Limits the weights formats these details apply to.
No Additional ItemsAdditional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: objectBioimage.io description of a Jupyter Notebook.
No Additional PropertiesA human-friendly name of the resource description
Must be at least 1 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
cover.png
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 1 characters long
🦈
🦥
file and other attachments
∈📦 File attachments
No Additional ItemsA URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Additional Properties of any type are allowed.
Type: objectcitations
No Additional Itemsfree text description
A digital object identifier (DOI) is the prefered citation reference.
See https://www.doi.org/ for details. (alternatively specify url)
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead)
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:
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 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)
bioimageio:
another_key:
nested: value
my_custom_key: 3837283
imagej:
macro_dir: path/to/macro/file
Each additional property must conform to the following schema
Type: objectEach additional property must conform to the following schema
Type: objectURL to download the resource from (deprecated)
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A URL to the Git repository where the resource is being developed.
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified authors are maintainers and at least some of them should specify their github_user name
Affiliation
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.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
version number (n-th published version, not the semantic version)
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.
"0.2.4"
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
∈📦 URL or relative path to a markdown file with additional documentation.
The recommended documentation file name is README.md. An .md suffix is mandatory.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
"notebook"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
The Jupyter notebook
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
Bioimage.io description of a Jupyter notebook.
No Additional PropertiesA human-friendly name of the resource description.
May only contains letters, digits, underscore, minus, parentheses and spaces.
Must be at least 1 characters long
Must be at most 128 characters long
A string containing a brief description.
Must be at most 1024 characters long
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 path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
UTF-8 emoji for display alongside the id.
Must be at least 1 characters long
Must be at most 2 characters long
🦈
🦥
file attachments
No Additional Items∈📦 file source
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
SHA256 checksum of the source file
A SHA-256 hash value
Must be at least 64 characters long
Must be at most 64 characters long
citations
Must contain a minimum of 1 items
A citation that should be referenced in work using this resource.
No Additional Propertiesfree text description
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.
A digital object identifier, see https://www.doi.org/
Must match regular expression:^10\.[0-9]{4}.+$
URL to cite (preferably specify a doi instead/also).
Note:
Either doi or url have to be specified.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A SPDX license identifier.
We do not support custom license beyond the SPDX license list, if you need that please
open a GitHub issue
to discuss your intentions with the community.
CC0-1.0
MIT
BSD-2-Clause
A URL to the Git repository where the resource is being developed.
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_descriptions/models/unet2d_nuclei_broad
An icon for illustration, e.g. on bioimage.io
Must be at least 1 characters long
Must be at most 2 characters long
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).
IDs of other bioimage.io resources
No Additional Items['ilastik/ilastik', 'deepimagej/deepimagej', 'zero/notebook_u-net_3d_zerocostdl4mic']
The person who uploaded the model (e.g. to bioimage.io)
name
Maintainers of this resource.
If not specified, authors are maintainers and at least some of them has to specify their github_user name
Affiliation
The version of the resource following SemVer 2.0.
wraps a packaging.version.Version instance for validation in pydantic models
The format version of this resource specification
"0.3.0"
∈📦 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.
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
https://raw.githubusercontent.com/bioimage-io/spec-bioimage-io/main/example_descriptions/models/unet2d_nuclei_broad/README.md
README.md
badges associated with this resource
No Additional ItemsA custom badge
No Additional Propertiesbadge label to display on hover
Open in Colab
badge icon
A path relative to the rdf.yaml file (also if the RDF source is a URL).
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/assets/colab-badge.svg
target URL
Must be at least 1 characters long
Must be at most 2083 characters long
https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/U-net_2D_ZeroCostDL4Mic.ipynb
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:
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 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.)
bioimage.io internal metadata.
Additional Properties of any type are allowed.
Type: objectAdditional Properties of any type are allowed.
Type: object"notebook"
bioimage.io-wide unique resource identifier
assigned by bioimage.io; version unspecific.
Must be at least 1 characters long
The description from which this one is derived
Must be at least 1 characters long
The Jupyter notebook
A URL with the HTTP or HTTPS scheme.
Must be at least 1 characters long
Must be at most 2083 characters long
A path relative to the rdf.yaml file (also if the RDF source is a URL).