Source code for torchgeo.models.resnet
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Pre-trained ResNet models."""
from typing import Any
import kornia.augmentation as K
import timm
import torch
from torch import nn
from torchvision.models._api import Weights, WeightsEnum
from .swin import (
_satlas_bands,
_satlas_sentinel2_bands,
_satlas_sentinel2_transforms,
_satlas_transforms,
)
# https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_TOA
_landsat_tm_toa_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']
# https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_TOA
_landsat_etm_toa_bands = [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6_VCID_1',
'B6_VCID_2',
'B7',
'B8',
]
# https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2
_landsat_etm_sr_bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7']
# https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA
_landsat_oli_tirs_toa_bands = [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B9',
'B10',
'B11',
]
# https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2
_landsat_oli_sr_bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']
# https://github.com/zhu-xlab/SSL4EO-S12/blob/main/src/download_data/convert_rgb.py
_sentinel2_toa_bands = [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B8a',
'B9',
'B10',
'B11',
'B12',
]
# https://github.com/zhu-xlab/SSL4EO-S12/blob/main/src/download_data/convert_rgb.py
_sentinel2_rgb_bands = ['B4', 'B3', 'B2']
# https://github.com/zhu-xlab/SSL4EO-S12/blob/main/src/download_data/convert_rgb.py
_sentinel1_grd_bands = ['VV', 'VH']
# https://github.com/zhu-xlab/DeCUR/blob/f190e9a3895ef645c005c8c2fce287ffa5a937e3/src/transfer_classification_BE/linear_BE_resnet.py#L286
# Normalization by channel-wise band statistics
_mean_s1 = torch.tensor([-12.59, -20.26])
_std_s1 = torch.tensor([5.26, 5.91])
_ssl4eo_s12_transforms_s1 = K.AugmentationSequential(
K.Resize((256, 256)),
K.CenterCrop(224),
K.Normalize(mean=_mean_s1, std=_std_s1),
data_keys=None,
)
# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167
# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97
# Normalization either by 10K (for S2 uint16 input) or channel-wise with band statistics
_ssl4eo_s12_transforms_s2_10k = K.AugmentationSequential(
K.Resize((256, 256)),
K.CenterCrop(224),
K.Normalize(mean=torch.tensor(0), std=torch.tensor(10000)),
data_keys=None,
)
_mean_s2 = torch.tensor(
[
1612.9,
1397.6,
1322.3,
1373.1,
1561.0,
2108.4,
2390.7,
2318.7,
2581.0,
837.7,
22.0,
2195.2,
1537.4,
]
)
_std_s2 = torch.tensor(
[
791.0,
854.3,
878.7,
1144.9,
1127.5,
1164.2,
1276.0,
1249.5,
1345.9,
577.5,
47.5,
1340.0,
1142.9,
]
)
_ssl4eo_s12_transforms_s2_stats = K.AugmentationSequential(
K.Resize((256, 256)),
K.CenterCrop(224),
K.Normalize(mean=_mean_s2, std=_std_s2),
data_keys=None,
)
# Normalization only available for RGB dataset, defined here:
# https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/seco_dataset.py
_min = torch.tensor([3, 2, 0])
_max = torch.tensor([88, 103, 129])
_mean = torch.tensor([0.485, 0.456, 0.406])
_std = torch.tensor([0.229, 0.224, 0.225])
_seco_transforms = K.AugmentationSequential(
K.Resize((256, 256)),
K.CenterCrop(224),
K.Normalize(mean=_min, std=_max - _min),
K.Normalize(mean=torch.tensor(0), std=1 / torch.tensor(255)),
K.Normalize(mean=_mean, std=_std),
data_keys=None,
)
# Normalization only available for RGB dataset, defined here:
# https://github.com/PlekhanovaElena/ssl4eco/blob/7445e048035f7ae31c0eb45e1ed8426c9989fe56/pretraining/pretrain_seco_3heads.py#L140
# https://github.com/PlekhanovaElena/ssl4eco/blob/7445e048035f7ae31c0eb45e1ed8426c9989fe56/downstream_tasks/test_modules/secoeco_test_module.py#L28
_seco_eco_transforms = K.AugmentationSequential(
K.Resize((224, 224)),
K.Normalize(mean=torch.tensor(0.0), std=torch.tensor(10000.0)),
data_keys=None,
)
# Normalization only available for RGB dataset, defined here:
# https://github.com/sustainlab-group/geography-aware-ssl/blob/main/moco_fmow/main_moco_geo%2Btp.py#L287
_mean = torch.tensor([0.485, 0.456, 0.406])
_std = torch.tensor([0.229, 0.224, 0.225])
_gassl_transforms = K.AugmentationSequential(
K.Resize((224, 224)),
K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
K.Normalize(mean=_mean, std=_std),
data_keys=None,
)
# https://github.com/torchgeo/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43
_ssl4eo_l_transforms = K.AugmentationSequential(
K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
K.CenterCrop((224, 224)),
data_keys=None,
)
[docs]class ResNet18_Weights(WeightsEnum): # type: ignore[misc]
"""ResNet-18 weights.
For `timm <https://github.com/huggingface/pytorch-image-models>`_
*resnet18* implementation.
.. versionadded:: 0.4
"""
LANDSAT_TM_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_moco-1c691b4f.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_tm_toa_bands,
},
)
LANDSAT_TM_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_simclr-d2d38ace.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_tm_toa_bands,
},
)
LANDSAT_ETM_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_moco-bb88689c.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 9,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_etm_toa_bands,
},
)
LANDSAT_ETM_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_simclr-4d813f79.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 9,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_etm_toa_bands,
},
)
LANDSAT_ETM_SR_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_moco-4f078acd.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 6,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_etm_sr_bands,
},
)
LANDSAT_ETM_SR_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_simclr-8e8543b4.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 6,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_etm_sr_bands,
},
)
LANDSAT_OLI_TIRS_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_moco-a3002f51.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 11,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_oli_tirs_toa_bands,
},
)
LANDSAT_OLI_TIRS_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_simclr-b0635cc6.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 11,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_oli_tirs_toa_bands,
},
)
LANDSAT_OLI_SR_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_moco-660e82ed.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_oli_sr_bands,
},
)
LANDSAT_OLI_SR_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_simclr-7bced5be.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_oli_sr_bands,
},
)
SENTINEL2_ALL_MOCO = Weights(
url='https://hf.co/torchgeo/resnet18_sentinel2_all_moco/resolve/5b8cddc9a14f3844350b7f40b85bcd32aed75918/resnet18_sentinel2_all_moco-59bfdff9.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 13,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'moco',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_RGB_MOCO = Weights(
url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_moco/resolve/e1c032e7785fd0625224cdb6699aa138bb304eec/resnet18_sentinel2_rgb_moco-e3a335e3.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 3,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'moco',
'bands': _sentinel2_rgb_bands,
},
)
SENTINEL2_RGB_SECO = Weights(
url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_seco/resolve/f8dcee692cf7142163b55a5c197d981fe0e717a0/resnet18_sentinel2_rgb_seco-cefca942.pth',
transforms=_seco_transforms,
meta={
'dataset': 'SeCo Dataset',
'in_chans': 3,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2103.16607',
'repo': 'https://github.com/ServiceNow/seasonal-contrast',
'ssl_method': 'seco',
'bands': _sentinel2_rgb_bands,
},
)
[docs]class ResNet50_Weights(WeightsEnum): # type: ignore[misc]
"""ResNet-50 weights.
For `timm <https://github.com/huggingface/pytorch-image-models>`_
*resnet50* implementation.
.. versionadded:: 0.4
"""
FMOW_RGB_GASSL = Weights(
url='https://hf.co/torchgeo/resnet50_fmow_rgb_gassl/resolve/fe8a91026cf9104f1e884316b8e8772d7af9052c/resnet50_fmow_rgb_gassl-da43d987.pth',
transforms=_gassl_transforms,
meta={
'dataset': 'fMoW Dataset',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2011.09980',
'repo': 'https://github.com/sustainlab-group/geography-aware-ssl',
'ssl_method': 'gassl',
},
)
LANDSAT_TM_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_moco-ba1ce753.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_tm_toa_bands,
},
)
LANDSAT_TM_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_simclr-a1c93432.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_tm_toa_bands,
},
)
LANDSAT_ETM_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_moco-e9a84d5a.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_etm_toa_bands,
},
)
LANDSAT_ETM_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_simclr-70b5575f.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_etm_toa_bands,
},
)
LANDSAT_ETM_SR_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_moco-1266cde3.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 6,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_etm_sr_bands,
},
)
LANDSAT_ETM_SR_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_simclr-e5d185d7.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 6,
'model': 'resnet18',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_etm_sr_bands,
},
)
LANDSAT_OLI_TIRS_TOA_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_moco-de7f5e0f.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 11,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_oli_tirs_toa_bands,
},
)
LANDSAT_OLI_TIRS_TOA_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_simclr-030cebfe.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 11,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_oli_tirs_toa_bands,
},
)
LANDSAT_OLI_SR_MOCO = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_moco-ff580dad.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'moco',
'bands': _landsat_oli_sr_bands,
},
)
LANDSAT_OLI_SR_SIMCLR = Weights(
url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_simclr-94f78913.pth',
transforms=_ssl4eo_l_transforms,
meta={
'dataset': 'SSL4EO-L',
'in_chans': 7,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2306.09424',
'repo': 'https://github.com/torchgeo/torchgeo',
'ssl_method': 'simclr',
'bands': _landsat_oli_sr_bands,
},
)
# ALL is deprecated, use GRD instead
SENTINEL1_ALL_DECUR = SENTINEL1_GRD_DECUR = Weights(
url='https://huggingface.co/torchgeo/decur/resolve/9328eeb90c686a88b30f8526ed757b4bc0f12027/rn50_ssl4eo-s12_sar_decur_ep100-f0e69ba2.pth',
transforms=_ssl4eo_s12_transforms_s1,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 2,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2309.05300',
'repo': 'https://github.com/zhu-xlab/DeCUR',
'ssl_method': 'decur',
'bands': _sentinel1_grd_bands,
},
)
# ALL is deprecated, use GRD instead
SENTINEL1_ALL_MOCO = SENTINEL1_GRD_MOCO = Weights(
url='https://hf.co/torchgeo/resnet50_sentinel1_all_moco/resolve/e79862c667853c10a709bdd77ea8ffbad0e0f1cf/resnet50_sentinel1_all_moco-906e4356.pth',
transforms=_ssl4eo_s12_transforms_s1,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 2,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'moco',
'bands': _sentinel1_grd_bands,
},
)
SENTINEL1_GRD_CLOSP = Weights(
url='https://huggingface.co/DarthReca/CLOSP-Visual/resolve/3bb8677c21dac56bea2dd7baa08d7871272db440/closp-rn_s1_encoder-cacc90bc.pth',
transforms=nn.Identity(),
meta={
'dataset': 'CrisisLandMark',
'in_chans': 2,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2507.10403',
'repo': 'https://github.com/DarthReca/closp',
'bands': _sentinel1_grd_bands,
},
)
SENTINEL1_GRD_GEOCLOSP = Weights(
url='https://huggingface.co/DarthReca/CLOSP-Visual/resolve/3bb8677c21dac56bea2dd7baa08d7871272db440/geoclosp-rn_s1_encoder-e63ae1af.pth',
transforms=nn.Identity(),
meta={
'dataset': 'CrisisLandMark',
'in_chans': 2,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2507.10403',
'repo': 'https://github.com/DarthReca/closp',
'bands': _sentinel1_grd_bands,
},
)
SENTINEL1_GRD_SOFTCON = Weights(
url='https://huggingface.co/wangyi111/softcon/resolve/62ff465b2e7467dbfc70758ec1e9d08ab87fc46b/B2_rn50_softcon.pth',
transforms=_ssl4eo_s12_transforms_s1,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 2,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2405.20462',
'repo': 'https://github.com/zhu-xlab/softcon',
'ssl_method': 'softcon',
'bands': _sentinel1_grd_bands,
},
)
SENTINEL2_ALL_CLOSP = Weights(
url='https://huggingface.co/DarthReca/CLOSP-Visual/resolve/3bb8677c21dac56bea2dd7baa08d7871272db440/closp-rn_s2_encoder-183922a5.pth',
transforms=K.AugmentationSequential(
K.Normalize(mean=0, std=10000), data_keys=None
),
meta={
'dataset': 'CrisisLandMark',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2507.10403',
'repo': 'https://github.com/DarthReca/closp',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_DECUR = Weights(
url='https://huggingface.co/torchgeo/decur/resolve/eba7ae5945d482a4319be046d34b552db5dd9950/rn50_ssl4eo-s12_ms_decur_ep100-fc6b09ff.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2309.05300',
'repo': 'https://github.com/zhu-xlab/DeCUR',
'ssl_method': 'decur',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_DINO = Weights(
url='https://hf.co/torchgeo/resnet50_sentinel2_all_dino/resolve/d7f14bf5530d70ac69d763e58e77e44dbecfec7c/resnet50_sentinel2_all_dino-d6c330e9.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'dino',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_GEOCLOSP = Weights(
url='https://huggingface.co/DarthReca/CLOSP-Visual/resolve/3bb8677c21dac56bea2dd7baa08d7871272db440/geoclosp-rn_s2_encoder-94c37f4a.pth',
transforms=K.AugmentationSequential(
K.Normalize(mean=0, std=10000), data_keys=None
),
meta={
'dataset': 'CrisisLandMark',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2507.10403',
'repo': 'https://github.com/DarthReca/closp',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_MOCO = Weights(
url='https://hf.co/torchgeo/resnet50_sentinel2_all_moco/resolve/da4f3c9dbe09272eb902f3b37f46635fa4726879/resnet50_sentinel2_all_moco-df8b932e.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'moco',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_SOFTCON = Weights(
url='https://huggingface.co/wangyi111/softcon/resolve/62ff465b2e7467dbfc70758ec1e9d08ab87fc46b/B13_rn50_softcon.pth',
transforms=_ssl4eo_s12_transforms_s2_stats,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 13,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2405.20462',
'repo': 'https://github.com/zhu-xlab/softcon',
'ssl_method': 'softcon',
'bands': _sentinel2_toa_bands,
},
)
SENTINEL2_ALL_SECO_ECO = Weights(
url='https://hf.co/torchgeo/seco-eco/resolve/aea279ea46572cfca5876ac1f9d8d8595fcdeb3b/resnet50_sentinel2_all_seco_eco-90ec322f.pth',
transforms=_seco_eco_transforms,
meta={
'dataset': 'SSL4Eco Dataset',
'in_chans': 12,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2504.18256',
'repo': 'https://github.com/PlekhanovaElena/ssl4eco',
'ssl_method': 'seco-eco',
'bands': [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B8A',
'B9',
'B11',
'B12',
],
},
)
SENTINEL2_ALL_NDVI_SECO_ECO = Weights(
url='https://hf.co/torchgeo/seco-eco-ndvi/resolve/44fae184c63b73e15a32be816e023957dc4c56c1/resnet50_sentinel2_all_ndvi_seco_eco-65292b83.pth',
transforms=_seco_eco_transforms,
meta={
'dataset': 'SSL4Eco Dataset',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2504.18256',
'repo': 'https://github.com/PlekhanovaElena/ssl4eco',
'ssl_method': 'seco-eco',
'bands': ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'NDVI'],
},
)
SENTINEL2_MI_MS_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet50_mi_ms-da5413d2.pth',
transforms=_satlas_sentinel2_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_sentinel2_bands,
},
)
SENTINEL2_MI_RGB_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet50_mi_rgb-e79bb7fe.pth',
transforms=_satlas_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_bands,
},
)
SENTINEL2_RGB_MOCO = Weights(
url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_moco/resolve/efd9723b59a88e9dc1420dc1e96afb25b0630a3c/resnet50_sentinel2_rgb_moco-2b57ba8b.pth',
transforms=_ssl4eo_s12_transforms_s2_10k,
meta={
'dataset': 'SSL4EO-S12',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.07044',
'repo': 'https://github.com/zhu-xlab/SSL4EO-S12',
'ssl_method': 'moco',
'bands': _sentinel2_rgb_bands,
},
)
SENTINEL2_RGB_SECO = Weights(
url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_seco/resolve/fbd07b02a8edb8fc1035f7957160deed4321c145/resnet50_sentinel2_rgb_seco-018bf397.pth',
transforms=_seco_transforms,
meta={
'dataset': 'SeCo Dataset',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2103.16607',
'repo': 'https://github.com/ServiceNow/seasonal-contrast',
'ssl_method': 'seco',
'bands': _sentinel2_rgb_bands,
},
)
SENTINEL2_SI_MS_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet50_si_ms-1f454cc6.pth',
transforms=_satlas_sentinel2_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_sentinel2_bands,
},
)
SENTINEL2_SI_RGB_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet50_si_rgb-45fc6972.pth',
transforms=_satlas_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_bands,
},
)
[docs]class ResNet152_Weights(WeightsEnum): # type: ignore[misc]
"""ResNet-152 weights.
For `timm <https://github.com/huggingface/pytorch-image-models>`_
*resnet152* implementation.
.. versionadded:: 0.6
"""
SENTINEL2_MI_MS_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet152_mi_ms-fd35b4bb.pth',
transforms=_satlas_sentinel2_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_sentinel2_bands,
},
)
SENTINEL2_MI_RGB_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet152_mi_rgb-67563ac5.pth',
transforms=_satlas_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_bands,
},
)
SENTINEL2_SI_MS_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet152_si_ms-4500c6cb.pth',
transforms=_satlas_sentinel2_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 9,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_sentinel2_bands,
},
)
SENTINEL2_SI_RGB_SATLAS = Weights(
url='https://hf.co/torchgeo/satlas/resolve/081d6607431bf36bdb59c223777cbb267131b8f2/sentinel2_resnet152_si_rgb-f4d24c3c.pth',
transforms=_satlas_transforms,
meta={
'dataset': 'SatlasPretrain',
'in_chans': 3,
'model': 'resnet50',
'publication': 'https://arxiv.org/abs/2211.15660',
'repo': 'https://github.com/allenai/satlaspretrain_models',
'bands': _satlas_bands,
},
)
[docs]def resnet18(
weights: ResNet18_Weights | None = None, *args: Any, **kwargs: Any
) -> nn.Module:
"""ResNet-18 model.
If you use this model in your research, please cite the following paper:
* https://arxiv.org/pdf/1512.03385
.. versionadded:: 0.4
Args:
weights: Pre-trained model weights to use.
*args: Additional arguments to pass to :func:`timm.create_model`
**kwargs: Additional keyword arguments to pass to :func:`timm.create_model`
Returns:
A ResNet-18 model.
"""
if weights:
kwargs['in_chans'] = weights.meta['in_chans']
model = timm.create_model('resnet18', *args, **kwargs)
if weights:
missing_keys, unexpected_keys = model.load_state_dict(
weights.get_state_dict(progress=True), strict=False
)
assert set(missing_keys) <= {'fc.weight', 'fc.bias'}
assert set(unexpected_keys) <= {'fc.weight', 'fc.bias'}
return model
[docs]def resnet50(
weights: ResNet50_Weights | None = None, *args: Any, **kwargs: Any
) -> nn.Module:
"""ResNet-50 model.
If you use this model in your research, please cite the following paper:
* https://arxiv.org/pdf/1512.03385
.. versionchanged:: 0.4
Switched to multi-weight support API.
Args:
weights: Pre-trained model weights to use.
*args: Additional arguments to pass to :func:`timm.create_model`.
**kwargs: Additional keyword arguments to pass to :func:`timm.create_model`.
Returns:
A ResNet-50 model.
"""
if weights:
kwargs['in_chans'] = weights.meta['in_chans']
model = timm.create_model('resnet50', *args, **kwargs)
if weights:
missing_keys, unexpected_keys = model.load_state_dict(
weights.get_state_dict(progress=True), strict=False
)
assert set(missing_keys) <= {'fc.weight', 'fc.bias'}
# used when features_only = True
assert set(unexpected_keys) <= {'fc.weight', 'fc.bias'}
return model
[docs]def resnet152(
weights: ResNet152_Weights | None = None, *args: Any, **kwargs: Any
) -> nn.Module:
"""ResNet-152 model.
If you use this model in your research, please cite the following paper:
* https://arxiv.org/pdf/1512.03385
.. versionadded:: 0.6
Args:
weights: Pre-trained model weights to use.
*args: Additional arguments to pass to :func:`timm.create_model`.
**kwargs: Additional keyword arguments to pass to :func:`timm.create_model`.
Returns:
A ResNet-152 model.
"""
if weights:
kwargs['in_chans'] = weights.meta['in_chans']
model = timm.create_model('resnet152', *args, **kwargs)
if weights:
missing_keys, unexpected_keys = model.load_state_dict(
weights.get_state_dict(progress=True), strict=False
)
assert set(missing_keys) <= {'fc.weight', 'fc.bias'}
# used when features_only = True
assert set(unexpected_keys) <= {'fc.weight', 'fc.bias'}
return model
