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Merge pull request #91 from RobustBench/add-models
add models from Addepalli2022Efficient
2 parents dca3b0f + 17b6515 commit 1b632c5

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‎README.md

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‎model_info/cifar10/Linf/Addepalli2022Efficient_RN18.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar10",
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"venue": "CVPRW 2022",
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"architecture": "ResNet-18",
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"eps": "8/255",
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"clean_acc": "85.71",
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"reported": "52.50",
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"autoattack_acc": "52.48",
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"unreliable": false
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}

‎model_info/cifar10/Linf/Addepalli2022Efficient_WRN_34_10.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar10",
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"venue": "CVPRW 2022",
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"architecture": "WideResNet-34-10",
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"eps": "8/255",
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"clean_acc": "88.71",
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"reported": "57.81",
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"autoattack_acc": "57.81",
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"unreliable": false
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}

‎model_info/cifar10/corruptions/Addepalli2022Efficient_WRN_34_10.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar10",
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"venue": "CVPRW 2022",
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"architecture": "WideResNet-34-10",
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"eps": null,
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"clean_acc": "88.71",
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"reported": "80.12",
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"corruptions_acc": "80.12"
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}

‎model_info/cifar10/corruptions/unaggregated_results.csv

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@@ -22,3 +22,4 @@ Diffenderfer2021Winning_Binary_CARD_Deck,0.945,0.9388,0.9229,0.9149,0.9036,0.937
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Addepalli2021Towards_WRN34,0.8466,0.8389,0.7994,0.7758,0.7151,0.8236,0.7945,0.7601,0.7581,0.724,0.8451,0.8379,0.8177,0.8048,0.7985,0.8448,0.8405,0.8333,0.8212,0.8016,0.8394,0.8172,0.7719,0.7423,0.7037,0.8459,0.8326,0.815,0.7972,0.7467,0.8587,0.8588,0.8568,0.8526,0.8317,0.8303,0.7687,0.684,0.5667,0.3517,0.816,0.8127,0.8043,0.7947,0.7759,0.8483,0.8285,0.7935,0.7843,0.7467,0.7998,0.8012,0.7935,0.7425,0.7465,0.8248,0.7948,0.7542,0.6549,0.551,0.8118,0.6633,0.5305,0.3584,0.1953,0.8379,0.8307,0.8279,0.8231,0.8195,0.8062,0.8101,0.7952,0.781,0.7728
2323
Modas2021PRIMEResNet18,0.9203,0.9157,0.8993,0.8975,0.8848,0.92,0.906,0.8883,0.8885,0.8677,0.918,0.899,0.8857,0.856,0.856,0.9222,0.9118,0.9073,0.8814,0.8356,0.9145,0.901,0.8902,0.8767,0.8686,0.928,0.9236,0.9165,0.907,0.8845,0.929,0.9275,0.925,0.9238,0.9148,0.9288,0.9173,0.898,0.8575,0.7277,0.9156,0.9163,0.9112,0.9063,0.8931,0.9211,0.9097,0.8953,0.894,0.8807,0.8737,0.8744,0.8802,0.8246,0.8343,0.9148,0.8877,0.8668,0.8048,0.743,0.9291,0.9172,0.9096,0.8936,0.8389,0.914,0.8997,0.8954,0.8903,0.8804,0.9055,0.908,0.8996,0.8799,0.8594
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Erichson2022NoisyMix,0.9611,0.9529,0.9339,0.9198,0.8991,0.9588,0.9463,0.9318,0.9327,0.9118,0.9581,0.9396,0.935,0.9166,0.9065,0.9594,0.95,0.9427,0.8862,0.7533,0.9548,0.9395,0.912,0.9003,0.8818,0.9668,0.9637,0.959,0.9513,0.9269,0.9682,0.9659,0.9636,0.9601,0.9493,0.9666,0.961,0.9509,0.9338,0.8563,0.9545,0.9536,0.9469,0.9417,0.9276,0.9621,0.9497,0.9329,0.9292,0.9076,0.8973,0.8964,0.9038,0.8305,0.8412,0.9662,0.9658,0.9647,0.9614,0.9618,0.9666,0.957,0.946,0.9136,0.6425,0.9429,0.9272,0.9201,0.9098,0.8909,0.9512,0.9506,0.945,0.9175,0.8805
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Addepalli2022Efficient_WRN_34_10,0.8819,0.8752,0.8474,0.8345,0.8038,0.8565,0.8319,0.7952,0.7941,0.7508,0.8752,0.8623,0.8473,0.8132,0.797,0.8798,0.8738,0.8701,0.8565,0.838,0.8769,0.8596,0.8286,0.8123,0.7974,0.8811,0.8657,0.8483,0.8315,0.7803,0.888,0.8859,0.8799,0.8693,0.8309,0.8674,0.7994,0.7051,0.5727,0.3483,0.8485,0.8429,0.8306,0.8208,0.8012,0.87,0.8425,0.8033,0.805,0.7798,0.8378,0.8365,0.8345,0.7796,0.7841,0.8618,0.8301,0.8075,0.7472,0.703,0.8498,0.6766,0.5126,0.3187,0.1941,0.873,0.866,0.8609,0.8576,0.8571,0.8401,0.84,0.8353,0.8175,0.8074

‎model_info/cifar100/Linf/Addepalli2022Efficient_RN18.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar100",
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"venue": "CVPRW 2022",
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"architecture": "ResNet-18",
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"eps": "8/255",
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"clean_acc": "65.45",
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"reported": "27.69",
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"autoattack_acc": "27.67",
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"unreliable": false
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}

‎model_info/cifar100/Linf/Addepalli2022Efficient_WRN_34_10.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar100",
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"venue": "CVPRW 2022",
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"architecture": "WideResNet-34-10",
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"eps": "8/255",
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"clean_acc": "68.75",
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"reported": "31.85",
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"autoattack_acc": "31.85",
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"unreliable": false
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}

‎model_info/cifar100/corruptions/Addepalli2022Efficient_WRN_34_10.json

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{
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"link": "https://artofrobust.github.io/short_paper/31.pdf",
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"name": "Efficient and Effective Augmentation Strategy for Adversarial Training",
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"authors": "Sravanti Addepalli, Samyak Jain, Venkatesh Babu Radhakrishnan",
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"additional_data": false,
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"number_forward_passes": 1,
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"dataset": "cifar100",
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"venue": "CVPRW 2022",
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"architecture": "WideResNet-34-10",
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"eps": null,
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"clean_acc": "68.75",
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"reported": "56.95",
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"corruptions_acc": "56.95"
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}

‎model_info/cifar100/corruptions/unaggregated_results.csv

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@@ -12,3 +12,4 @@ Addepalli2021Towards_PARN18,0.6211,0.6115,0.5906,0.5595,0.4787,0.5629,0.3907,0.2
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Addepalli2021Towards_WRN34,0.6612,0.6483,0.629,0.601,0.5244,0.601,0.413,0.2831,0.1501,0.0367,0.6479,0.6225,0.5948,0.5679,0.5046,0.5824,0.5841,0.5723,0.5581,0.5497,0.6252,0.521,0.3998,0.2813,0.1164,0.6244,0.5759,0.508,0.507,0.4457,0.6502,0.6324,0.5878,0.5586,0.5251,0.5992,0.5995,0.5866,0.5378,0.5375,0.6286,0.592,0.551,0.4678,0.3738,0.6358,0.6241,0.6194,0.6153,0.6076,0.6104,0.5701,0.5238,0.5243,0.4813,0.6477,0.6413,0.6321,0.6204,0.5883,0.6534,0.65,0.6141,0.5872,0.5317,0.6522,0.6274,0.6021,0.5634,0.5248,0.5955,0.5896,0.5718,0.5582,0.5327
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Modas2021PRIMEResNet18,0.7739,0.7698,0.7601,0.7442,0.7095,0.7709,0.7508,0.7316,0.7032,0.604,0.7733,0.7593,0.7371,0.707,0.639,0.7184,0.7261,0.7147,0.6767,0.6272,0.7717,0.7484,0.7074,0.6518,0.4926,0.7423,0.7127,0.6769,0.6801,0.6395,0.7315,0.7068,0.6725,0.6565,0.6407,0.623,0.6319,0.6361,0.5417,0.5471,0.7361,0.68,0.6207,0.5216,0.4336,0.7184,0.677,0.6553,0.6399,0.6178,0.7458,0.7153,0.6769,0.6785,0.6433,0.7563,0.7286,0.7057,0.6267,0.5212,0.7488,0.7284,0.7001,0.6911,0.6648,0.7527,0.7045,0.6824,0.6457,0.6284,0.7428,0.7341,0.718,0.697,0.6658
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Erichson2022NoisyMix,0.7887,0.7659,0.6938,0.664,0.597,0.7888,0.7653,0.7323,0.7305,0.6957,0.7925,0.74,0.7366,0.7,0.656,0.7977,0.7792,0.7606,0.6877,0.5489,0.7666,0.7109,0.6427,0.6046,0.5732,0.8101,0.8017,0.7868,0.7634,0.7187,0.8097,0.8034,0.7896,0.7744,0.7241,0.81,0.7935,0.758,0.7166,0.5775,0.7809,0.7793,0.766,0.7496,0.721,0.7835,0.7417,0.6895,0.6884,0.6334,0.6286,0.6355,0.6528,0.5229,0.5414,0.8111,0.8104,0.8087,0.8033,0.8018,0.8071,0.7716,0.7376,0.6672,0.3828,0.7563,0.7151,0.6997,0.6826,0.6571,0.7624,0.7724,0.7612,0.7121,0.6538
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Addepalli2022Efficient_WRN_34_10,0.6816,0.6689,0.6102,0.5803,0.5064,0.6409,0.5995,0.5611,0.5587,0.5168,0.6695,0.6484,0.6201,0.5767,0.5478,0.6785,0.6701,0.6641,0.649,0.6196,0.6762,0.6414,0.578,0.5358,0.497,0.6749,0.652,0.6244,0.6013,0.5432,0.6855,0.6786,0.663,0.6389,0.5722,0.6583,0.569,0.4518,0.3217,0.1393,0.6286,0.62,0.6027,0.5904,0.5669,0.6558,0.6155,0.5626,0.5606,0.5112,0.6163,0.6185,0.6118,0.5383,0.552,0.617,0.5381,0.4629,0.3326,0.2355,0.6424,0.4941,0.3669,0.2089,0.0425,0.6658,0.6531,0.6487,0.6427,0.637,0.6138,0.6131,0.6048,0.589,0.5807

‎robustbench/model_zoo/cifar10.py

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std=CIFAR10_STD),
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'gdrive_id': '1uQZYUuUiL9BzaQUeXLhjr_RhoyFRrHe3'
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}),
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('Addepalli2022Efficient_RN18', {
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'model': ResNet18,
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'gdrive_id': '1m5vhdzIUUKhDbsZdOG9z76Eyp6f4xe_f',
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}),
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('Addepalli2022Efficient_WRN_34_10', {
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'model': lambda: WideResNet(depth=34, widen_factor=10),
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'gdrive_id': '1--dVDtZhAk4D2zMtTDwIGnImuCGxTcBA',
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}),
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])
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l2 = OrderedDict([
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'model': Modas2021PRIMEResNet18,
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'gdrive_id': '13oDyqi16FeXy5j4Vm6IghnjTVqp_XF5U'
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}),
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('Addepalli2022Efficient_WRN_34_10', {
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'model': lambda: WideResNet(depth=34, widen_factor=10),
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'gdrive_id': '1--dVDtZhAk4D2zMtTDwIGnImuCGxTcBA',
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}),
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])
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cifar_10_models = OrderedDict([(ThreatModel.Linf, linf), (ThreatModel.L2, l2),

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