{ "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" ] }