update

ensemble/ensem_4_4_flex.json

@@ -1,286 +0,0 @@
-{
-  "type": "Feature",
-  "stac_version": "1.1.0",
-  "stac_extensions": [
-    "https://stac-extensions.github.io/mlm/v1.5.0/schema.json",
-    "https://stac-extensions.github.io/file/v2.1.0/schema.json"
-  ],
-  "id": "ENSEMBLE_4MODELS_FLEXIBLE_20251202",
-  "geometry": {
-    "type": "Polygon",
-    "coordinates": [
-      [
-        [
-          -180.0,
-          -90.0
-        ],
-        [
-          -180.0,
-          90.0
-        ],
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-          180.0,
-          90.0
-        ],
-        [
-          180.0,
-          -90.0
-        ],
-        [
-          -180.0,
-          -90.0
-        ]
-      ]
-    ]
-  },
-  "bbox": [
-    -180,
-    -90,
-    180,
-    90
-  ],
-  "properties": {
-    "datetime": "2025-12-02T12:03:10Z",
-    "created": "2025-12-02T12:03:10Z",
-    "updated": "2025-12-02T12:03:10Z",
-    "description": "Flexible ensemble of 4 models (1dpwdeeplabv3, 1dpwunetpp, 1dpwseg, unet) with runtime aggregation (mean/max/min) and uncertainty quantification for cloud detection in VGT-1, VGT-2, and PROBA-V satellite imagery. Models are loaded separately and combined at inference time.",
-    "title": "Ensemble Cloud Detection Model (4 Models + Uncertainty) - VGT1/VGT2/Proba-V",
-    "mlm:name": "ensemble_4models_flexible_fdr4vgt_cloudmask",
-    "mlm:architecture": "Flexible Ensemble (runtime Mean/Max/Min + Uncertainty): 1dpwdeeplabv3, 1dpwunetpp, 1dpwseg, unet",
-    "mlm:tasks": [
-      "semantic-segmentation",
-      "uncertainty-quantification"
-    ],
-    "mlm:framework": "pytorch",
-    "mlm:framework_version": "2.5.1+cu121",
-    "mlm:accelerator": "cuda",
-    "mlm:accelerator_constrained": false,
-    "mlm:accelerator_summary": "NVIDIA GPU with CUDA support (compute capability >= 7.0)",
-    "mlm:accelerator_count": 1,
-    "mlm:batch_size_suggestion": 4,
-    "mlm:pretrained": true,
-    "mlm:input": [
-      {
-        "name": "VGT_PROBA_TOC_reflectance",
-        "bands": [
-          "Blue (B0, ~450nm)",
-          "Red (B2, ~645nm)",
-          "Near-Infrared (B3, ~835nm)",
-          "SWIR (MIR, ~1665nm)"
-        ],
-        "input": {
-          "shape": [
-            -1,
-            4,
-            512,
-            512
-          ],
-          "dim_order": [
-            "batch",
-            "channel",
-            "height",
-            "width"
-          ],
-          "data_type": "float32"
-        },
-        "norm": {
-          "type": "raw_toc_reflectance",
-          "range": [
-            0,
-            10000
-          ],
-          "description": "Raw Top-of-Canopy reflectance values scaled by 10000"
-        },
-        "pre_processing_function": null
-      }
-    ],
-    "mlm:output": [
-      {
-        "name": "cloud_probability",
-        "tasks": [
-          "semantic-segmentation"
-        ],
-        "result": {
-          "shape": [
-            -1,
-            1,
-            512,
-            512
-          ],
-          "dim_order": [
-            "batch",
-            "channel",
-            "height",
-            "width"
-          ],
-          "data_type": "float32"
-        },
-        "classification:classes": [
-          {
-            "value": 0.0,
-            "name": "clear",
-            "description": "Clear sky (may contain cloud shadows)",
-            "color_hint": "00000000"
-          },
-          {
-            "value": 1.0,
-            "name": "cloud",
-            "description": "Cloud present",
-            "color_hint": "FFFF00"
-          }
-        ],
-        "post_processing_function": "Apply threshold to get binary mask. Recommended threshold: 0.5051. Returns tuple: (probabilities, uncertainty)",
-        "standard_threshold": 0.5,
-        "recommended_threshold": 0.5051,
-        "value_range": [
-          0.0,
-          1.0
-        ],
-        "description": "Per-pixel mean probability across 4 ensemble models. Built-in sigmoid activation. Values close to 1.0 indicate high confidence of cloud."
-      },
-      {
-        "name": "prediction_uncertainty",
-        "tasks": [
-          "uncertainty-quantification"
-        ],
-        "result": {
-          "shape": [
-            -1,
-            1,
-            512,
-            512
-          ],
-          "dim_order": [
-            "batch",
-            "channel",
-            "height",
-            "width"
-          ],
-          "data_type": "float32"
-        },
-        "value_range": [
-          0.0,
-          1.0
-        ],
-        "description": "Normalized standard deviation across 4 ensemble members. Values close to 1.0 indicate high disagreement between models (high uncertainty). Automatically returned as second element of output tuple."
-      }
-    ],
-    "custom:export_format": "torch.export.pt2",
-    "custom:has_sigmoid": true,
-    "custom:sigmoid_location": "built-in wrapper",
-    "custom:project": "FDR4VGT",
-    "custom:project_url": "https://fdr4vgt.eu/",
-    "custom:sensors": [
-      "VGT-1",
-      "VGT-2",
-      "PROBA-V"
-    ],
-    "custom:sensor_notes": "Model applicable to SPOT-VGT1, SPOT-VGT2, and PROBA-V imagery",
-    "custom:spatial_resolution": "1km",
-    "custom:tile_size": 512,
-    "custom:recommended_overlap": 64,
-    "custom:applicable_start": "1998-03-01T00:00:00Z",
-    "custom:applicable_end": null,
-    "dependencies": [
-      "torch>=2.0.0",
-      "segmentation-models-pytorch>=0.3.0",
-      "pytorch-lightning>=2.0.0"
-    ],
-    "custom:export_datetime": "2025-12-02T12:03:10Z",
-    "custom:ensemble_size": 4,
-    "custom:ensemble_members": [
-      "1dpwdeeplabv3",
-      "1dpwunetpp",
-      "1dpwseg",
-      "unet"
-    ],
-    "custom:ensemble_strategy": "Flexible runtime aggregation - supports mean/max/min modes with uncertainty quantification",
-    "custom:ensemble_fused": false,
-    "custom:returns_tuple": true,
-    "custom:tuple_format": "(probabilities, uncertainty)"
-  },
-  "links": [
-    {
-      "rel": "about",
-      "href": "https://fdr4vgt.eu/",
-      "type": "text/html",
-      "title": "FDR4VGT Project - Harmonized VGT Data Record"
-    },
-    {
-      "rel": "license",
-      "href": "https://creativecommons.org/licenses/by/4.0/",
-      "type": "text/html",
-      "title": "CC-BY-4.0 License"
-    }
-  ],
-  "assets": {
-    "load": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/load.py",
-      "type": "application/x-python-code",
-      "title": "Ensemble model loader",
-      "description": "Python code to load all models and combine them into an EnsembleModel class with mean/max/min aggregation.",
-      "roles": [
-        "code"
-      ]
-    },
-    "example_data": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/example_data.safetensor",
-      "type": "application/octet-stream; application=safetensors",
-      "title": "Example VGT/PROBA-V image",
-      "description": "Example VGT/PROBA-V Top-of-Canopy reflectance image for model inference.",
-      "roles": [
-        "mlm:example_data",
-        "data"
-      ]
-    },
-    "model_01_1dpwdeeplabv3": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/f_global_1dpwdeeplabv3.pt2",
-      "type": "application/octet-stream; application=pytorch",
-      "title": "Model 1: 1dpwdeeplabv3_fdr4vgt_cloudmask_ft",
-      "description": "The weights of the 1DPWDEEPLABV3 model in torch.export .pt2 format with built-in sigmoid activation.",
-      "mlm:artifact_type": "torch.export.pt2",
-      "roles": [
-        "mlm:model",
-        "mlm:weights",
-        "data"
-      ]
-    },
-    "model_02_1dpwunetpp": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/f_global_1dpwunetpp.pt2",
-      "type": "application/octet-stream; application=pytorch",
-      "title": "Model 2: 1dpwunetpp_fdr4vgt_cloudmask_ft",
-      "description": "The weights of the 1DPWUNETPP model in torch.export .pt2 format with built-in sigmoid activation.",
-      "mlm:artifact_type": "torch.export.pt2",
-      "roles": [
-        "mlm:model",
-        "mlm:weights",
-        "data"
-      ]
-    },
-    "model_03_1dpwseg": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/f_global_1dpwseg.pt2",
-      "type": "application/octet-stream; application=pytorch",
-      "title": "Model 3: 1dpwseg_fdr4vgt_cloudmask_ft",
-      "description": "The weights of the 1DPWSEG model in torch.export .pt2 format with built-in sigmoid activation.",
-      "mlm:artifact_type": "torch.export.pt2",
-      "roles": [
-        "mlm:model",
-        "mlm:weights",
-        "data"
-      ]
-    },
-    "model_04_unet": {
-      "href": "https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/f_global_unet.pt2",
-      "type": "application/octet-stream; application=pytorch",
-      "title": "Model 4: unet_fdr4vgt_cloudmask_ft",
-      "description": "The weights of the UNET model in torch.export .pt2 format with built-in sigmoid activation.",
-      "mlm:artifact_type": "torch.export.pt2",
-      "roles": [
-        "mlm:model",
-        "mlm:weights",
-        "data"
-      ]
-    }
-  },
-  "collection": "FDR4VGT_CloudMask_Ensemble_Flexible_4models"
-}