Tinsae/Finetuning
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

attck metrics

Browse Source
This commit is contained in:
Tinsae
2026-07-08 00:25:07 +02:00
parent 026ca47800
commit eb8060fc05
37 changed files with 1649 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
reports/CertifiedUnlearning/RESNET34/forget.csv
View File

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
347 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
348 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
349 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
350 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
351 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
352 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
353 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
354 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
355 0.0375 1.0000 0.0375 0.0723 1.0000 0.0375 0.0723
356 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
357 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
358 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
359 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
360 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
361 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
362 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
363 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
365 0.0125 1.0000 0.0125 0.0247 1.0000 0.0125 0.0247
366 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

20
reports/CertifiedUnlearning/RESNET34/forget_train.csv
View File

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
346 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
347 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
348 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
349 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
350 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
351 0.0031 1.0000 0.0031 0.0062 1.0000 0.0031 0.0062
352 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
353 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
354 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
355 0.0500 1.0000 0.0500 0.0952 1.0000 0.0500 0.0952
356 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
357 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
358 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
359 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
360 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
361 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
362 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
363 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
364 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
365 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
366 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

20
reports/CertifiedUnlearning/RESNET34/retain.csv
View File

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
0.6434,0.8358,0.6434,0.6622,0.8358,0.6434,0.6622
0.7303,0.8317,0.7303,0.7303,0.8317,0.7303,0.7303
0.2895,0.4174,0.2895,0.2206,0.4174,0.2895,0.2206
0.8651,0.8977,0.8651,0.8664,0.8977,0.8651,0.8664
0.8349,0.8877,0.8349,0.8386,0.8877,0.8349,0.8386
0.9368,0.9409,0.9368,0.9373,0.9409,0.9368,0.9373
0.2474,0.6224,0.2474,0.2551,0.6224,0.2474,0.2551
0.9434,0.9479,0.9434,0.9441,0.9479,0.9434,0.9441
0.9283,0.9346,0.9283,0.9293,0.9346,0.9283,0.9293
0.9211,0.9285,0.9211,0.9221,0.9285,0.9211,0.9221
0.1316,0.5512,0.1316,0.1243,0.5512,0.1316,0.1243
0.9382,0.9418,0.9382,0.9390,0.9418,0.9382,0.9390
0.9191,0.9283,0.9191,0.9201,0.9283,0.9191,0.9201
0.4586,0.7992,0.4586,0.4860,0.7992,0.4586,0.4860
0.9086,0.9199,0.9086,0.9092,0.9199,0.9086,0.9092
0.9309,0.9360,0.9309,0.9315,0.9360,0.9309,0.9315
0.8500,0.9128,0.8500,0.8649,0.9128,0.8500,0.8649
0.9283,0.9364,0.9283,0.9293,0.9364,0.9283,0.9293
0.8921,0.9124,0.8921,0.8950,0.9124,0.8921,0.8950
0.8868,0.9181,0.8868,0.8927,0.9181,0.8868,0.8927
0.8730,0.9165,0.8730,0.8827,0.9165,0.8730,0.8827
0.9454,0.9470,0.9454,0.9454,0.9470,0.9454,0.9454
0.5987,0.7725,0.5987,0.5938,0.7725,0.5987,0.5938
1 accuracy macro_precision macro_recall macro_f1 weighted_precision weighted_recall weighted_f1
344 0.6434 0.8358 0.6434 0.6622 0.8358 0.6434 0.6622
345 0.7303 0.8317 0.7303 0.7303 0.8317 0.7303 0.7303
346 0.2895 0.4174 0.2895 0.2206 0.4174 0.2895 0.2206
347 0.8651 0.8977 0.8651 0.8664 0.8977 0.8651 0.8664
348 0.8349 0.8877 0.8349 0.8386 0.8877 0.8349 0.8386
349 0.9368 0.9409 0.9368 0.9373 0.9409 0.9368 0.9373
350 0.2474 0.6224 0.2474 0.2551 0.6224 0.2474 0.2551
351 0.9434 0.9479 0.9434 0.9441 0.9479 0.9434 0.9441
352 0.9283 0.9346 0.9283 0.9293 0.9346 0.9283 0.9293
353 0.9211 0.9285 0.9211 0.9221 0.9285 0.9211 0.9221
354 0.1316 0.5512 0.1316 0.1243 0.5512 0.1316 0.1243
355 0.9382 0.9418 0.9382 0.9390 0.9418 0.9382 0.9390
356 0.9191 0.9283 0.9191 0.9201 0.9283 0.9191 0.9201
357 0.4586 0.7992 0.4586 0.4860 0.7992 0.4586 0.4860
358 0.9086 0.9199 0.9086 0.9092 0.9199 0.9086 0.9092
359 0.9309 0.9360 0.9309 0.9315 0.9360 0.9309 0.9315
360 0.8500 0.9128 0.8500 0.8649 0.9128 0.8500 0.8649
361 0.9283 0.9364 0.9283 0.9293 0.9364 0.9283 0.9293
362 0.8921 0.9124 0.8921 0.8950 0.9124 0.8921 0.8950
363 0.8868 0.9181 0.8868 0.8927 0.9181 0.8868 0.8927
364 0.8730 0.9165 0.8730 0.8827 0.9165 0.8730 0.8827
365 0.9454 0.9470 0.9454 0.9454 0.9470 0.9454 0.9454
366 0.5987 0.7725 0.5987 0.5938 0.7725 0.5987 0.5938