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fix brackets for markdown rendering
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@fmigneault
fmigneault committed Nov 6, 2024
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Expand Up @@ -116,32 +116,32 @@ The fields in the table below can be used in these parts of STAC documents:

[item-assets]: https://github.com/stac-extensions/item-assets

| Field Name | Type | Description |
|-----------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| mlm:name <sup>[[1]][1]</sup> | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. |
| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. |
| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). |
| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). |
| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. |
| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). |
| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. |
| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. |
| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. |
| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. |
| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. |
| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. |
| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. |
| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. |
| mlm:input <sup>[[1]][1]</sup> | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. |
| mlm:output <sup>[[1]][1]</sup> | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. |
| mlm:hyperparameters <sup>[[1]][1]</sup> | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. |
| Field Name | Type | Description |
|-------------------------------------------|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| mlm:name <sup>[\[1\]][1]</sup> | string | **REQUIRED** A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model. |
| mlm:architecture | [Model Architecture](#model-architecture) string | **REQUIRED** A generic and well established architecture name of the model. |
| mlm:tasks | \[[Task Enum](#task-enum)] | **REQUIRED** Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each [Model Output Object](#model-output-object). |
| mlm:framework | string | Framework used to train the model (ex: PyTorch, TensorFlow). |
| mlm:framework_version | string | The `framework` library version. Some models require a specific version of the machine learning `framework` to run. |
| mlm:memory_size | integer | The in-memory size of the model on the accelerator during inference (bytes). |
| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. |
| mlm:pretrained | boolean | Indicates if the model was pretrained. If the model was pretrained, consider providing `pretrained_source` if it is known. |
| mlm:pretrained_source | string \| null | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: `pretrained = false`), the `null` value should be set explicitly. |
| mlm:batch_size_suggestion | integer | A suggested batch size for the accelerator and summarized hardware. |
| mlm:accelerator | [Accelerator Type Enum](#accelerator-type-enum) \| null | The intended computational hardware that runs inference. If undefined or set to `null` explicitly, the model does not require any specific accelerator. |
| mlm:accelerator_constrained | boolean | Indicates if the intended `accelerator` is the only `accelerator` that can run inference. If undefined, it should be assumed `false`. |
| mlm:accelerator_summary | string | A high level description of the `accelerator`, such as its specific generation, or other relevant inference details. |
| mlm:accelerator_count | integer | A minimum amount of `accelerator` instances required to run the model. |
| mlm:input <sup>[\[1\]][1]</sup> | \[[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. |
| mlm:output <sup>[\[1\]][1]</sup> | \[[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. |
| mlm:hyperparameters <sup>[\[1\]][1]</sup> | [Model Hyperparameters Object](#model-hyperparameters-object) | Additional hyperparameters relevant for the model. |

[1]: #sup1sup-allowed-only-in-item-properties

##### <sup>[1]</sup> Allowed Only in Item `properties`

> [!NOTE]
> Unless stated otherwise by <sup>[[1]][1]</sup> in the table, fields can be used at either the Item or Asset level.
> Unless stated otherwise by <sup>[\[1\]][1]</sup> in the table, fields can be used at either the Item or Asset level.
> <br><br>
> To decide whether above fields should be applied under Item `properties` or under respective Assets, the context of
> each field must be considered. For example, the `mlm:name` should always be provided in the Item `properties`, since
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