|
{ |
|
"type": "Feature", |
|
"stac_version": "1.0.0", |
|
"id": "item_landcover_eurosat_sentinel2", |
|
"properties": { |
|
"start_datetime": "1900-01-01T00:00:00Z", |
|
"end_datetime": "9999-01-01T00:00:00Z", |
|
"description": "Sourced from torchgeo python library, identifier is ResNet18_Weights.SENTINEL2_ALL_MOCO. The batch size suggestion is 3300, which almost maxes out an NVIDIA 3090's 24 GB CUDA memory.", |
|
"mlm:framework": "pytorch", |
|
"mlm:framework_version": "2.3.0+cu121", |
|
"file:size": 91000000, |
|
"mlm:memory_size": 94452432, |
|
"mlm:batch_size_suggestion": 3300, |
|
"mlm:accelerator": "cuda", |
|
"mlm:accelerator_constrained": false, |
|
"mlm:accelerator_summary": "Unknown", |
|
"mlm:name": "Resnet-18 Sentinel-2 ALL MOCO", |
|
"mlm:architecture": "ResNet-18", |
|
"mlm:tasks": [ |
|
"scene-classification" |
|
], |
|
"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_by_channel": true, |
|
"norm_type": "z-score", |
|
"statistics": [ |
|
{ |
|
"mean": 1354.40546513, |
|
"stddev": 245.71762908 |
|
}, |
|
{ |
|
"mean": 1118.24399958, |
|
"stddev": 333.00778264 |
|
}, |
|
{ |
|
"mean": 1042.92983953, |
|
"stddev": 395.09249139 |
|
}, |
|
{ |
|
"mean": 947.62620298, |
|
"stddev": 593.75055589 |
|
}, |
|
{ |
|
"mean": 1199.47283961, |
|
"stddev": 566.4170017 |
|
}, |
|
{ |
|
"mean": 1999.79090914, |
|
"stddev": 861.18399006 |
|
}, |
|
{ |
|
"mean": 2369.22292565, |
|
"stddev": 1086.63139075 |
|
}, |
|
{ |
|
"mean": 2296.82608323, |
|
"stddev": 1117.98170791 |
|
}, |
|
{ |
|
"mean": 732.08340178, |
|
"stddev": 404.91978886 |
|
}, |
|
{ |
|
"mean": 12.11327804, |
|
"stddev": 4.77584468 |
|
}, |
|
{ |
|
"mean": 1819.01027855, |
|
"stddev": 1002.58768311 |
|
}, |
|
{ |
|
"mean": 1118.92391149, |
|
"stddev": 761.30323499 |
|
}, |
|
{ |
|
"mean": 2594.14080798, |
|
"stddev": 1231.58581042 |
|
} |
|
], |
|
"pre_processing_function": { |
|
"format": "python", |
|
"expression": "torchgeo.datamodules.eurosat.EuroSATDataModule.collate_fn" |
|
} |
|
} |
|
], |
|
"mlm:output": [ |
|
{ |
|
"name": "scene-classification", |
|
"tasks": [ |
|
"scene-classification" |
|
], |
|
"result": { |
|
"shape": [ |
|
-1, |
|
10 |
|
], |
|
"dim_order": [ |
|
"batch", |
|
"class" |
|
], |
|
"data_type": "float32" |
|
}, |
|
"classification:classes": [ |
|
{ |
|
"value": 0, |
|
"name": "Annual Crop", |
|
"description": "Annual Crop" |
|
}, |
|
{ |
|
"value": 1, |
|
"name": "Forest", |
|
"description": "Forest" |
|
}, |
|
{ |
|
"value": 2, |
|
"name": "Herbaceous Vegetation", |
|
"description": "Herbaceous Vegetation" |
|
}, |
|
{ |
|
"value": 3, |
|
"name": "Highway", |
|
"description": "Highway" |
|
}, |
|
{ |
|
"value": 4, |
|
"name": "Industrial Buildings", |
|
"description": "Industrial Buildings" |
|
}, |
|
{ |
|
"value": 5, |
|
"name": "Pasture", |
|
"description": "Pasture" |
|
}, |
|
{ |
|
"value": 6, |
|
"name": "Permanent Crop", |
|
"description": "Permanent Crop" |
|
}, |
|
{ |
|
"value": 7, |
|
"name": "Residential Buildings", |
|
"description": "Residential Buildings" |
|
}, |
|
{ |
|
"value": 8, |
|
"name": "River", |
|
"description": "River" |
|
}, |
|
{ |
|
"value": 9, |
|
"name": "SeaLake", |
|
"description": "SeaLake" |
|
} |
|
], |
|
"post_processing_function": null |
|
} |
|
], |
|
"mlm:total_parameters": 11700000, |
|
"mlm:pretrained": true, |
|
"mlm:pretrained_source": "EuroSat Sentinel-2", |
|
"datetime": null |
|
}, |
|
"geometry": { |
|
"type": "Polygon", |
|
"coordinates": [ |
|
[ |
|
[ |
|
-7.882190080512502, |
|
37.13739173208318 |
|
], |
|
[ |
|
-7.882190080512502, |
|
58.21798141355221 |
|
], |
|
[ |
|
27.911651652899923, |
|
58.21798141355221 |
|
], |
|
[ |
|
27.911651652899923, |
|
37.13739173208318 |
|
], |
|
[ |
|
-7.882190080512502, |
|
37.13739173208318 |
|
] |
|
] |
|
] |
|
}, |
|
"links": [ |
|
{ |
|
"rel": "derived_from", |
|
"href": "https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a", |
|
"type": "application/json" |
|
}, |
|
{ |
|
"rel": "self", |
|
"href": "s3://wherobots-modelhub-prod/community/classification/landcover-eurosat-sentinel2/model-metadata.json/item_landcover_eurosat_sentinel2.json", |
|
"type": "application/json" |
|
} |
|
], |
|
"assets": { |
|
"model": { |
|
"href": "s3://wherobots-modelhub-prod/community/classification/landcover-eurosat-sentinel2/scripting/model.pt", |
|
"type": "application/octet-stream; application=pytorch", |
|
"title": "Pytorch weights checkpoint", |
|
"description": "A Resnet-18 classification model trained on normalized Sentinel-2 imagery with Eurosat landcover labels with torchgeo.", |
|
"mlm_artifact_type": "torch.jit.script", |
|
"file:size": 43000000, |
|
"roles": [ |
|
"mlm:model", |
|
"data" |
|
] |
|
}, |
|
"source_code": { |
|
"href": "https://github.com/microsoft/torchgeo/blob/61efd2e2c4df7ebe3bd03002ebbaeaa3cfe9885a/torchgeo/models/resnet.py#L207", |
|
"type": "text/x-python", |
|
"title": "Model implementation.", |
|
"description": "Source code to run the model.", |
|
"roles": [ |
|
"mlm:model", |
|
"code" |
|
] |
|
} |
|
}, |
|
"bbox": [ |
|
-7.882190080512502, |
|
37.13739173208318, |
|
27.911651652899923, |
|
58.21798141355221 |
|
], |
|
"stac_extensions": [ |
|
"https://stac-extensions.github.io/file/v2.1.0/schema.json", |
|
"https://crim-ca.github.io/mlm-extension/v1.2.0/schema.json" |
|
] |
|
} |