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remove parameters, add artifact type field
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@rbavery
rbavery committed Mar 26, 2024
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Expand Up @@ -56,15 +56,10 @@ Check the original technical report for an earlier version of the Model Extensio
| mlm:accelerator | [Accelerator Enum](#accelerator-enum) | **REQUIRED.** The intended computational hardware that runs inference. |
| mlm:accelerator_constrained | boolean | **REQUIRED.** True if the intended `accelerator` is the only `accelerator` that can run inference. False if other accelerators, such as amd64 (CPU), can run inference. |
| mlm:hardware_summary | string | **REQUIRED.** A high level description of the number of accelerators, specific generation of the `accelerator`, or other relevant inference details. |
| mlm:model | [Asset Object](stac-asset) | **REQUIRED.** Asset object containing URI to the model file. |
| mlm:source_code | [Asset Object](stac-asset) | **REQUIRED.** Source code description. Can describe a github repo, zip archive, etc. |
| mlm:container | [Asset Object](stac-asset) | **RECOMMENDED.** Information to run the model in a container with URI to the container. |
| mlm:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. |
| mlm:pretrained_source | string | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. |
| mlm:summary | string | Text summary of the model and it's purpose. |
| batch_size_suggestion | number | A suggested batch size for the accelerator and summarized hardware. |
| mlm:parameters | [Parameters Object](#parameters-object) | Mapping with names for the parameters and their values. The field should be specified here if parameters apply to all [Model Input Objects](#model-input-object). If each [Model Input Object](#model-input-object) has parameters, specify parameters in that object. |


In addition, fields from the following extensions must be imported in the item:
- [Scientific Extension Specification][stac-ext-sci] to describe relevant publications.
Expand All @@ -82,7 +77,6 @@ In addition, fields from the following extensions must be imported in the item:
| name | string | **REQUIRED.** Informative name of the input variable. Example "RGB Time Series" | |
| bands | [string] | **REQUIRED.** The names of the raster bands used to train or fine-tune the model, which may be all or a subset of bands available in a STAC Item's [Band Object](#bands-and-statistics). | |
| input_array | [Array Object](#feature-array-object) | **REQUIRED.** The N-dimensional array object that describes the shape, dimension ordering, and data type. | |
| parameters | [Parameters Object](#parameters-object) | Mapping with names for the parameters and their values. Some models may take additional scalars, tuples, and other non-tensor inputs like text. | |
| norm_by_channel | boolean | Whether to normalize each channel by channel-wise statistics or to normalize by dataset statistics. If True, use an array of [Statistics Objects](#bands-and-statistics) that is ordered like the `bands` field in this object. | |
| norm_type | string | Normalization method. Select one option from `min_max`, `z_score`, `max_norm`, `mean_norm`, `unit_variance`, `norm_with_clip`, `none` | |
| resize_type | string | High-level descriptor of the rescaling method to change image shape. Select one option from `crop`, `pad`, `interpolation`, `none`. If your rescaling method combines more than one of these operations, provide the name of the operation instead | |
Expand All @@ -104,14 +98,23 @@ It is recommended to define `accelerator` with one of the following values:

[stac-asset]: https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object


### MLM Asset Fields

| mlm:model | [Asset Object](stac-asset) | **REQUIRED.** Asset object containing URI to the model file. |
| mlm:source_code | [Asset Object](stac-asset) | **REQUIRED.** Source code description. Can describe a github repo, zip archive, etc. |
| mlm:container | [Asset Object](stac-asset) | **RECOMMENDED.** Information to run the model in a container with URI to the container. |


### mlm:model Asset

| Field Name | Type | Description |
|------------|----------|---------------------------------------------------------------------------|
| title | string | Description of the model asset. |
| href | string | Url to the checkpoint or model artifact. |
| href | string | Url to the model artifact. |
| type | string | "application/x-pytorch" or specify another appropriate custom media type. |
| roles | [string] | Specify one or more of ["model", "weights", "compiled"] |
| mlm:artifact_type | ArtifactTypeEnum | Specifies the kind of model artifact. Typically related to a particular ml framework. |


Recommended asset `roles` include `weights` for model weights that need to be loaded by a model definition and `compiled` for models that can be loaded directly without an intermediate model definition.
Expand Down Expand Up @@ -165,13 +168,6 @@ You can also use other base images. Pytorch and Tensorflow offer docker images f
- [Torchserve](https://pytorch.org/serve/)
- [TFServing](https://github.com/tensorflow/serving)

### Parameters Object

| Field Name | Type | Description |
|---------------------------------------|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| *parameter names depend on the model* | number `\|` string `\|` boolean `\|` array | The number of fields and their names depend on the model. Values should not be n-dimensional array inputs. If the model input can be represented as an n-dimensional array, it should instead be supplied as another [model input object](#model-input-object). |

The `Parameters Object` is a user defined mapping of parameters to parameter values. This is meant to capture model inputs that can't be represented as n-dimensional arrays/tensors. This includes inputs like scalars, text, and booleans. The `parameters` field can either be specified in the [Model Input Object](#model-input-object) if they are associated with a specific input or as an [Item or Collection](#item-properties-and-collection-fields) field if the parameters are supplied without relation to a specific model input. For example: the [Segment Anything](https://ai.meta.com/blog/segment-anything-foundation-model-image-segmentation/) foundational model accepts a label integer for each image input.

#### Bands and Statistics

Expand Down

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