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add validation of MLM output using classification:classes (fixes #48) #50

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merged 6 commits into from
May 14, 2024
Merged

add validation of MLM output using classification:classes (fixes #48) #50

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b1d8bbc
add validation of MLM output using classification:classes (fixes #48)
fmigneault Apr 18, 2024
6d545bf
update MLM schema reference with published URI
fmigneault Apr 23, 2024
29032ba
add readme and changelog entries
fmigneault May 13, 2024
c1baa5d
Merge branch 'main' into add-mlm-output
fmigneault May 13, 2024
c10239a
add MLM reference to readme
fmigneault May 14, 2024
f24a4c1
Merge branch 'add-mlm-output' of github-perso:crim-ca/classification …
fmigneault May 14, 2024
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377 changes: 377 additions & 0 deletions examples/item-model-classes.json
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Original file line number Diff line number Diff line change
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{
"stac_version": "1.0.0",
"stac_extensions": [
"https://stac-extensions.github.io/mlm/v1.0.0/schema.json",
"https://stac-extensions.github.io/raster/v1.1.0/schema.json",
"https://stac-extensions.github.io/file/v1.0.0/schema.json",
"https://stac-extensions.github.io/ml-aoi/v0.2.0/schema.json",
"https://stac-extensions.github.io/classification/v1.1.0/schema.json"
],
"type": "Feature",
"id": "resnet-18_sentinel-2_all_moco_classification",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
-7.882190080512502,
37.13739173208318
],
[
-7.882190080512502,
58.21798141355221
],
[
27.911651652899923,
58.21798141355221
],
[
27.911651652899923,
37.13739173208318
],
[
-7.882190080512502,
37.13739173208318
]
]
]
},
"bbox": [
-7.882190080512502,
37.13739173208318,
27.911651652899923,
58.21798141355221
],
"properties": {
"description": "Sourced from torchgeo python library, identifier is ResNet18_Weights.SENTINEL2_ALL_MOCO",
"datetime": null,
"start_datetime": "1900-01-01T00:00:00Z",
"end_datetime": "9999-12-31T23:59:59Z",
"mlm:name": "Resnet-18 Sentinel-2 ALL MOCO",
"mlm:tasks": [
"classification"
],
"mlm:architecture": "ResNet",
"mlm:framework": "pytorch",
"mlm:framework_version": "2.1.2+cu121",
"file:size": 43000000,
"mlm:memory_size": 1,
"mlm:total_parameters": 11700000,
"mlm:pretrained_source": "EuroSat Sentinel-2",
"mlm:accelerator": "cuda",
"mlm:accelerator_constrained": false,
"mlm:accelerator_summary": "Unknown",
"mlm:batch_size_suggestion": 256,
"mlm:input": [
{
"name": "13 Band Sentinel-2 Batch",
"bands": [
"B01",
"B02",
"B03",
"B04",
"B05",
"B06",
"B07",
"B08",
"B8A",
"B09",
"B10",
"B11",
"B12"
],
"input": {
"shape": [
-1,
13,
64,
64
],
"dim_order": [
"batch",
"channel",
"height",
"width"
],
"data_type": "float32"
},
"norm_type": null,
"resize_type": null,
"pre_processing_function": {
"format": "python",
"expression": "torchgeo.datamodules.eurosat.EuroSATDataModule.collate_fn"
}
}
],
"mlm:output": [
{
"name": "classification",
"tasks": [
"classification"
],
"result": {
"shape": [
-1,
10
],
"dim_order": [
"batch",
"class"
],
"data_type": "float32"
},
"classification:classes": [
{
"value": 0,
"name": "AnnualCrop",
"color_hint": "FFFF00",
"nodata": false,
"title": "Annual Crop",
"description": "Represents areas of annual crops with a bright yellow color."
},
{
"value": 1,
"name": "Forest",
"color_hint": "008000",
"nodata": false,
"title": "Forest",
"description": "Depicts forested areas with a deep green color."
},
{
"value": 2,
"name": "HerbaceousVegetation",
"color_hint": "ADFF2F",
"nodata": false,
"title": "Herbaceous Vegetation",
"description": "Indicates areas of herbaceous vegetation with a green-yellow hue."
},
{
"value": 3,
"name": "Highway",
"color_hint": "808080",
"nodata": false,
"title": "Gray",
"description": "Denotes highways and roads with a neutral gray color."
},
{
"value": 4,
"name": "Industrial",
"color_hint": "800080",
"nodata": false,
"title": "Industrial Buildings",
"description": "Highlights industrial buildings with a vibrant purple color."
},
{
"value": 5,
"name": "Pasture",
"color_hint": "7CFC00",
"nodata": false,
"title": "Pasture",
"description": "Illustrates pasture areas with a fresh lawn green color."
},
{
"value": 6,
"name": "PermanentCrop",
"color_hint": "006400",
"nodata": false,
"title": "Permanent Crop",
"description": "Represents permanent crop areas with a dark green color."
},
{
"value": 7,
"name": "Residential",
"color_hint": "FF0000",
"nodata": false,
"title": "Residential Buildings",
"description": "Marks residential buildings with a bold red color."
},
{
"value": 8,
"name": "River",
"color_hint": "00FFFF",
"nodata": false,
"title": "River",
"description": "Depicts rivers and water bodies with a vivid cyan color."
},
{
"value": 9,
"name": "SeaLake",
"color_hint": "0000FF",
"nodata": false,
"title": "Sea and Lake",
"description": "Indicates seas and lakes with a serene blue color."
}
],
"post_processing_function": null
}
],
"raster:bands": [
{
"name": "B01",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 60,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B02",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 10,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B03",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 10,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B04",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 10,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B05",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B06",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B07",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B08",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 10,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B8A",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B09",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 60,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B10",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 60,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B11",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
},
{
"name": "B12",
"nodata": 0,
"data_type": "uint16",
"bits_per_sample": 15,
"spatial_resolution": 20,
"scale": 0.0001,
"offset": 0,
"unit": "m"
}
]
},
"assets": {
"weights": {
"href": "https://huggingface.co/torchgeo/resnet18_sentinel2_all_moco/resolve/main/resnet18_sentinel2_all_moco-59bfdff9.pth",
"title": "Pytorch weights checkpoint",
"description": "A Resnet-18 classification model trained on normalized Sentinel-2 imagery with Eurosat landcover labels with torchgeo",
"type": "application/octet-stream; application=pytorch",
"roles": [
"mlm:model",
"mlm:weights"
]
},
"source_code": {
"href": "https://github.com/microsoft/torchgeo/blob/61efd2e2c4df7ebe3bd03002ebbaeaa3cfe9885a/torchgeo/models/resnet.py#L207",
"title": "Model implementation.",
"description": "Source code to run the model.",
"type": "text/x-python",
"roles": [
"mlm:model",
"code",
"metadata"
]
}
},
"links": [
{
"rel": "self",
"href": "resnet-18_sentinel-2_all_moco_classification.json",
"type": "application/geo+json"
},
{
"rel": "derived_from",
"href": "https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a",
"type": "application/json",
"ml-aoi:split": "train"
}
]
}
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