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item.json
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{
"stac_version": "1.0.0",
"stac_extensions": [
"https://schemas.stacspec.org/v1.0.0-beta.3/extensions/dl-model/json-schema/schema.json",
"https://stac-extensions.github.io/eo/v1.1.0/schema.json",
"https://stac-extensions.github.io/processing/v1.1.0/schema.json",
"https://stac-extensions.github.io/scientific/v1.0.0/schema.json"
],
"id": "dlm-resnet18-unet-scse",
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
180,
90
],
[
-180,
-90
],
[
-180,
90
],
[
180,
90
],
[
180,
-90
]
]
]
},
"bbox": [
-180,
-90,
180,
90
],
"collection": "EO-DL-model-catalog",
"links": [
{
"rel": "self",
"href": "https://landsat-stac.s3.amazonaws.com/some-eo-models/example-thelper-item.json"
},
{
"rel": "collection",
"href": "https://landsat-stac.s3.amazonaws.com/some-eo-models/catalog.json"
}
],
"assets": [
{
"model_archive": {
"href": "https://drive.google.com/file/d/1PYyZVgH95454sb9LYHSfchbg8GuT__fR/view?usp=sharing",
"type": "application/zip",
"title": "model archive",
"description": "model archive as a google drive link",
"role": [
"model artefact"
]
}
}
],
"properties": {
"datetime": "2016-05-03T13:22:30Z",
"title": "resnet18+unet_scse",
"description": "UNet architecture with a resnet18 backbone and a SCSE layer fine-tuned on Pleiade imagery",
"license": "MIT",
"created": "2020-12-12T00:00:01.000Z",
"updated": "2021-01-04T00:30:55.000Z",
"providers": [
{
"name": "Effigis Inc.",
"roles": [
"image licensor"
],
"url": "https://effigis.com/en/"
},
{
"name": "Airbus Inc.",
"roles": [
"image provider"
],
"url": "https://www.intelligence-airbusds.com/"
}
],
"platform": "Pleiade",
"gsd": 0.5,
"eo:bands": [
{
"name": "50-cm panchromatic",
"common_name": "pancro",
"center_wavelength": 400
},
{
"name": "blue",
"common_name": "blue",
"center_wavelength": 490
},
{
"name": "green",
"common_name": "green",
"center_wavelength": 500
},
{
"name": "red",
"common_name": "red",
"center_wavelength": 660
},
{
"name": "Near Infrared",
"common_name": "nir",
"center_wavelength": 850
}
],
"sci:publications": [
{
"citation": "Abhijit Guha Roy and Nassir Navab and Christian Wachinger (2018). Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, arXiv 1803.02579",
"doi": "10.1007/978-3-030-00928-1_48"
}
],
"processing:level": "L4",
"dlm:runtime": {
"framework": "PyTorch",
"version": 1.5,
"model_handler": "thelper.cli.inference_session",
"model_src_url": "https://github.com/crim-ca/gin-model-repo",
"model_commit_hash": null,
"requirement_file": null,
"docker": {
"docker_file": "https://github.com/crim-ca/CCCOT03/blob/main/docker/thelper-geo.dockerfile",
"image_name": "thelper-geo:latest",
"tag": null,
"gpu": true,
"working_dir": "/workspace",
"run": "thelper infer --config /workspace/config.yml --save-dir /workspace --ckpt-path /workspace/ckpt.best.pth"
}
},
"dlm:archive": [
{
"name": "config.yml",
"role": [
"config file"
]
},
{
"name": "./test_pleiade/256/input_test.tif",
"role": [
"test set"
]
},
{
"name": "ckpt.best.pth",
"role": [
"model weight"
]
}
],
"dlm:data": {
"process_level": "ortho",
"dtype": "uint16",
"number_of_bands": 4,
"useful_bands": [
{
"index": 2,
"name": "red"
},
{
"index": 1,
"name": "green"
},
{
"index": 3,
"name": "nir"
}
],
"nodata_value": 0,
"test_file": "input_test.tif",
"item_examples": [
{
"title": "a pleiade stac item",
"url": "https://example.com/stac/pleiade/item.json"
}
]
},
"dlm:inputs": {
"name": "data",
"input_tensors": {
"batch": 1,
"channels": 3,
"height": 224,
"width": 224
},
"scaling_factor": 0.003921569,
"normalization:mean": [
0.245,
0.34,
0.67
],
"normalization:std": [
0.1,
0.1,
0.2
],
"selected_bands": [
0,
1,
3
],
"pre_processing_function": null
},
"dlm:outputs": {
"task": "semantic segmentation",
"number_of_classes": 5,
"dont_care_index": 0,
"final_layer_size": [
1,
5,
64,
64
],
"class_name_mapping": [
{
"0": "dontcare"
},
{
"1": "Bare Exposed Rock"
},
{
"2": "High density Residential"
},
{
"3": "Cropland"
},
{
"4": "Mixed Forest Land"
},
{
"5": "Lake"
}
],
"post_processing_function": null
},
"dlm:architecture": {
"total_nb_parameters": 42813873,
"estimated_total_size_mb": 183.72,
"type": "unet-resnet-18-scse",
"pretrained": "imagenet",
"summary": "----------------------------------------------------------------\n Layer (type) Output Shape Param\n================================================================\n Conv2d-1 [-1, 64, 32, 32] 9,408\n BatchNorm2d-2 [-1, 64, 32, 32] 128\n ReLU-3 [-1, 64, 32, 32] 0\n MaxPool2d-4 [-1, 64, 16, 16] 0\n Conv2d-5 [-1, 64, 16, 16] 36,864\n BatchNorm2d-6 [-1, 64, 16, 16] 128\n ReLU-7 [-1, 64, 16, 16] 0\n Conv2d-8 [-1, 64, 16, 16] 36,864\n BatchNorm2d-9 [-1, 64, 16, 16] 128\n ReLU-10 [-1, 64, 16, 16] 0\n BasicBlock-11 [-1, 64, 16, 16] 0\n Conv2d-12 [-1, 64, 16, 16] 36,864\n BatchNorm2d-13 [-1, 64, 16, 16] 128\n ReLU-14 [-1, 64, 16, 16] 0\n Conv2d-15 [-1, 64, 16, 16] 36,864\n BatchNorm2d-16 [-1, 64, 16, 16] 128\n ReLU-17 [-1, 64, 16, 16] 0\n BasicBlock-18 [-1, 64, 16, 16] 0\n Conv2d-19 [-1, 128, 8, 8] 73,728\n BatchNorm2d-20 [-1, 128, 8, 8] 256\n ReLU-21 [-1, 128, 8, 8] 0\n Conv2d-22 [-1, 128, 8, 8] 147,456\n BatchNorm2d-23 [-1, 128, 8, 8] 256\n Conv2d-24 [-1, 128, 8, 8] 8,192\n BatchNorm2d-25 [-1, 128, 8, 8] 256\n ReLU-26 [-1, 128, 8, 8] 0\n BasicBlock-27 [-1, 128, 8, 8] 0\n Conv2d-28 [-1, 128, 8, 8] 147,456\n BatchNorm2d-29 [-1, 128, 8, 8] 256\n ReLU-30 [-1, 128, 8, 8] 0\n Conv2d-31 [-1, 128, 8, 8] 147,456\n BatchNorm2d-32 [-1, 128, 8, 8] 256\n ReLU-33 [-1, 128, 8, 8] 0\n BasicBlock-34 [-1, 128, 8, 8] 0\n Conv2d-35 [-1, 256, 4, 4] 294,912\n BatchNorm2d-36 [-1, 256, 4, 4] 512\n ReLU-37 [-1, 256, 4, 4] 0\n Conv2d-38 [-1, 256, 4, 4] 589,824\n BatchNorm2d-39 [-1, 256, 4, 4] 512\n Conv2d-40 [-1, 256, 4, 4] 32,768\n BatchNorm2d-41 [-1, 256, 4, 4] 512\n ReLU-42 [-1, 256, 4, 4] 0\n BasicBlock-43 [-1, 256, 4, 4] 0\n Conv2d-44 [-1, 256, 4, 4] 589,824\n BatchNorm2d-45 [-1, 256, 4, 4] 512\n ReLU-46 [-1, 256, 4, 4] 0\n Conv2d-47 [-1, 256, 4, 4] 589,824\n BatchNorm2d-48 [-1, 256, 4, 4] 512\n ReLU-49 [-1, 256, 4, 4] 0\n BasicBlock-50 [-1, 256, 4, 4] 0\n Conv2d-51 [-1, 512, 2, 2] 1,179,648\n BatchNorm2d-52 [-1, 512, 2, 2] 1,024\n ReLU-53 [-1, 512, 2, 2] 0\n Conv2d-54 [-1, 512, 2, 2] 2,359,296\n BatchNorm2d-55 [-1, 512, 2, 2] 1,024\n Conv2d-56 [-1, 512, 2, 2] 131,072\n BatchNorm2d-57 [-1, 512, 2, 2] 1,024\n ReLU-58 [-1, 512, 2, 2] 0\n BasicBlock-59 [-1, 512, 2, 2] 0\n Conv2d-60 [-1, 512, 2, 2] 2,359,296\n BatchNorm2d-61 [-1, 512, 2, 2] 1,024\n ReLU-62 [-1, 512, 2, 2] 0\n Conv2d-63 [-1, 512, 2, 2] 2,359,296\n BatchNorm2d-64 [-1, 512, 2, 2] 1,024\n ReLU-65 [-1, 512, 2, 2] 0\n BasicBlock-66 [-1, 512, 2, 2] 0\n MaxPool2d-67 [-1, 512, 1, 1] 0\n Conv2d-68 [-1, 1024, 1, 1] 4,719,616\n BatchNorm2d-69 [-1, 1024, 1, 1] 2,048\n ReLU-70 [-1, 1024, 1, 1] 0\n_ActivatedBatchNorm-71 [-1, 1024, 1, 1] 0 AdaptiveAvgPool2d-72 [-1, 1024, 1, 1] 0\n Linear-73 [-1, 64] 65,600\n ReLU-74 [-1, 64] 0\n Linear-75 [-1, 1024] 66,560\n Conv2d-76 [-1, 1, 1, 1] 1,024\n SCSEBlock-77 [-1, 1024, 1, 1] 0\n ConvTranspose2d-78 [-1, 512, 2, 2] 8,389,120\n DecoderUnetSCSE-79 [-1, 512, 2, 2] 0\n Conv2d-80 [-1, 1024, 2, 2] 9,438,208\n BatchNorm2d-81 [-1, 1024, 2, 2] 2,048\n ReLU-82 [-1, 1024, 2, 2] 0\n_ActivatedBatchNorm-83 [-1, 1024, 2, 2] 0 AdaptiveAvgPool2d-84 [-1, 1024, 1, 1] 0\n Linear-85 [-1, 64] 65,600\n ReLU-86 [-1, 64] 0\n Linear-87 [-1, 1024] 66,560\n Conv2d-88 [-1, 1, 2, 2] 1,024\n SCSEBlock-89 [-1, 1024, 2, 2] 0\n ConvTranspose2d-90 [-1, 256, 4, 4] 4,194,560\n DecoderUnetSCSE-91 [-1, 256, 4, 4] 0\n Conv2d-92 [-1, 512, 4, 4] 2,359,808\n BatchNorm2d-93 [-1, 512, 4, 4] 1,024\n ReLU-94 [-1, 512, 4, 4] 0\n_ActivatedBatchNorm-95 [-1, 512, 4, 4] 0 AdaptiveAvgPool2d-96 [-1, 512, 1, 1] 0\n Linear-97 [-1, 32] 16,416\n ReLU-98 [-1, 32] 0\n Linear-99 [-1, 512] 16,896\n Conv2d-100 [-1, 1, 4, 4] 512\n SCSEBlock-101 [-1, 512, 4, 4] 0\nConvTranspose2d-102 [-1, 128, 8, 8] 1,048,704 DecoderUnetSCSE-103 [-1, 128, 8, 8] 0\n Conv2d-104 [-1, 256, 8, 8] 590,080\n BatchNorm2d-105 [-1, 256, 8, 8] 512\n ReLU-106 [-1, 256, 8, 8] 0\n_ActivatedBatchNorm-107 [-1, 256, 8, 8] 0 AdaptiveAvgPool2d-108 [-1, 256, 1, 1] 0\n Linear-109 [-1, 16] 4,112\n ReLU-110 [-1, 16] 0\n Linear-111 [-1, 256] 4,352\n Conv2d-112 [-1, 1, 8, 8] 256\n SCSEBlock-113 [-1, 256, 8, 8] 0\nConvTranspose2d-114 [-1, 64, 16, 16] 262,208 DecoderUnetSCSE-115 [-1, 64, 16, 16] 0\n Conv2d-116 [-1, 128, 16, 16] 147,584\n BatchNorm2d-117 [-1, 128, 16, 16] 256\n ReLU-118 [-1, 128, 16, 16] 0\n_ActivatedBatchNorm-119 [-1, 128, 16, 16] 0 AdaptiveAvgPool2d-120 [-1, 128, 1, 1] 0\n Linear-121 [-1, 8] 1,032\n ReLU-122 [-1, 8] 0\n Linear-123 [-1, 128] 1,152\n Conv2d-124 [-1, 1, 16, 16] 128\n SCSEBlock-125 [-1, 128, 16, 16] 0\nConvTranspose2d-126 [-1, 32, 32, 32] 65,568 DecoderUnetSCSE-127 [-1, 32, 32, 32] 0\n Conv2d-128 [-1, 64, 32, 32] 55,360\n BatchNorm2d-129 [-1, 64, 32, 32] 128\n ReLU-130 [-1, 64, 32, 32] 0\n_ActivatedBatchNorm-131 [-1, 64, 32, 32] 0 AdaptiveAvgPool2d-132 [-1, 64, 1, 1] 0\n Linear-133 [-1, 4] 260\n ReLU-134 [-1, 4] 0\n Linear-135 [-1, 64] 320\n Conv2d-136 [-1, 1, 32, 32] 64\n SCSEBlock-137 [-1, 64, 32, 32] 0\nConvTranspose2d-138 [-1, 16, 64, 64] 16,400 DecoderUnetSCSE-139 [-1, 16, 64, 64] 0\n Conv2d-140 [-1, 64, 64, 64] 31,808\n BatchNorm2d-141 [-1, 64, 64, 64] 128\n ReLU-142 [-1, 64, 64, 64] 0\n_ActivatedBatchNorm-143 [-1, 64, 64, 64] 0\n Conv2d-144 [-1, 5, 64, 64] 325\nEncoderDecoderNet-145 [-1, 5, 64, 64] 0 ================================================================ Total params= 42,813,873 Trainable params= 42,813,873 Non-trainable params= 0 ---------------------------------------------------------------- Input size (MB)= 0.05 Forward/backward pass size (MB)= 20.35 Params size (MB)= 163.32 Estimated Total Size (MB)= 183.72 ----------------------------------------------------------------"
}
}
}