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"과적 합"을 방지하는 Pytorch를 사용하여 Faster RCNN (COCO 데이터 세트)에서 훈련 된 최상의 모델을 저장합니다.

debugcn 에 게시 Dev
6
호세 데이비드

Pytorch를 사용하여 COCO 데이터 세트에서 Faster RCNN 신경망을 훈련하고 있습니다.

다음 튜토리얼을 따랐습니다 : https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

교육 결과는 다음과 같습니다.

Epoch: [6]  [  0/119]  eta: 0:01:16  lr: 0.000050  loss: 0.3780 (0.3780)  loss_classifier: 0.1290 (0.1290)  loss_box_reg: 0.1848 (0.1848)  loss_objectness: 0.0239 (0.0239)  loss_rpn_box_reg: 0.0403 (0.0403)  time: 0.6451  data: 0.1165  max mem: 3105
Epoch: [6]  [ 10/119]  eta: 0:01:13  lr: 0.000050  loss: 0.4129 (0.4104)  loss_classifier: 0.1277 (0.1263)  loss_box_reg: 0.2164 (0.2059)  loss_objectness: 0.0244 (0.0309)  loss_rpn_box_reg: 0.0487 (0.0473)  time: 0.6770  data: 0.1253  max mem: 3105
Epoch: [6]  [ 20/119]  eta: 0:01:07  lr: 0.000050  loss: 0.4165 (0.4302)  loss_classifier: 0.1277 (0.1290)  loss_box_reg: 0.2180 (0.2136)  loss_objectness: 0.0353 (0.0385)  loss_rpn_box_reg: 0.0499 (0.0491)  time: 0.6843  data: 0.1265  max mem: 3105
Epoch: [6]  [ 30/119]  eta: 0:01:00  lr: 0.000050  loss: 0.4205 (0.4228)  loss_classifier: 0.1271 (0.1277)  loss_box_reg: 0.2125 (0.2093)  loss_objectness: 0.0334 (0.0374)  loss_rpn_box_reg: 0.0499 (0.0484)  time: 0.6819  data: 0.1274  max mem: 3105
Epoch: [6]  [ 40/119]  eta: 0:00:53  lr: 0.000050  loss: 0.4127 (0.4205)  loss_classifier: 0.1209 (0.1265)  loss_box_reg: 0.2102 (0.2085)  loss_objectness: 0.0315 (0.0376)  loss_rpn_box_reg: 0.0475 (0.0479)  time: 0.6748  data: 0.1282  max mem: 3105
Epoch: [6]  [ 50/119]  eta: 0:00:46  lr: 0.000050  loss: 0.3973 (0.4123)  loss_classifier: 0.1202 (0.1248)  loss_box_reg: 0.1947 (0.2039)  loss_objectness: 0.0315 (0.0366)  loss_rpn_box_reg: 0.0459 (0.0470)  time: 0.6730  data: 0.1297  max mem: 3105
Epoch: [6]  [ 60/119]  eta: 0:00:39  lr: 0.000050  loss: 0.3900 (0.4109)  loss_classifier: 0.1206 (0.1248)  loss_box_reg: 0.1876 (0.2030)  loss_objectness: 0.0345 (0.0365)  loss_rpn_box_reg: 0.0431 (0.0467)  time: 0.6692  data: 0.1276  max mem: 3105
Epoch: [6]  [ 70/119]  eta: 0:00:33  lr: 0.000050  loss: 0.3984 (0.4085)  loss_classifier: 0.1172 (0.1242)  loss_box_reg: 0.2069 (0.2024)  loss_objectness: 0.0328 (0.0354)  loss_rpn_box_reg: 0.0458 (0.0464)  time: 0.6707  data: 0.1252  max mem: 3105
Epoch: [6]  [ 80/119]  eta: 0:00:26  lr: 0.000050  loss: 0.4153 (0.4113)  loss_classifier: 0.1178 (0.1246)  loss_box_reg: 0.2123 (0.2036)  loss_objectness: 0.0328 (0.0364)  loss_rpn_box_reg: 0.0480 (0.0468)  time: 0.6744  data: 0.1264  max mem: 3105
Epoch: [6]  [ 90/119]  eta: 0:00:19  lr: 0.000050  loss: 0.4294 (0.4107)  loss_classifier: 0.1178 (0.1238)  loss_box_reg: 0.2098 (0.2021)  loss_objectness: 0.0418 (0.0381)  loss_rpn_box_reg: 0.0495 (0.0466)  time: 0.6856  data: 0.1302  max mem: 3105
Epoch: [6]  [100/119]  eta: 0:00:12  lr: 0.000050  loss: 0.4295 (0.4135)  loss_classifier: 0.1171 (0.1235)  loss_box_reg: 0.2124 (0.2034)  loss_objectness: 0.0460 (0.0397)  loss_rpn_box_reg: 0.0498 (0.0469)  time: 0.6955  data: 0.1345  max mem: 3105
Epoch: [6]  [110/119]  eta: 0:00:06  lr: 0.000050  loss: 0.4126 (0.4117)  loss_classifier: 0.1229 (0.1233)  loss_box_reg: 0.2119 (0.2024)  loss_objectness: 0.0430 (0.0394)  loss_rpn_box_reg: 0.0481 (0.0466)  time: 0.6822  data: 0.1306  max mem: 3105
Epoch: [6]  [118/119]  eta: 0:00:00  lr: 0.000050  loss: 0.4006 (0.4113)  loss_classifier: 0.1171 (0.1227)  loss_box_reg: 0.2028 (0.2028)  loss_objectness: 0.0366 (0.0391)  loss_rpn_box_reg: 0.0481 (0.0466)  time: 0.6583  data: 0.1230  max mem: 3105
Epoch: [6] Total time: 0:01:20 (0.6760 s / it)
creating index...
index created!
Test:  [ 0/59]  eta: 0:00:15  model_time: 0.1188 (0.1188)  evaluator_time: 0.0697 (0.0697)  time: 0.2561  data: 0.0634  max mem: 3105
Test:  [58/59]  eta: 0:00:00  model_time: 0.1086 (0.1092)  evaluator_time: 0.0439 (0.0607)  time: 0.2361  data: 0.0629  max mem: 3105
Test: Total time: 0:00:14 (0.2378 s / it)
Averaged stats: model_time: 0.1086 (0.1092)  evaluator_time: 0.0439 (0.0607)
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.643
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.079
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.011
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.096
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.333
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.333
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Epoch: [7]  [  0/119]  eta: 0:01:16  lr: 0.000050  loss: 0.3851 (0.3851)  loss_classifier: 0.1334 (0.1334)  loss_box_reg: 0.1845 (0.1845)  loss_objectness: 0.0287 (0.0287)  loss_rpn_box_reg: 0.0385 (0.0385)  time: 0.6433  data: 0.1150  max mem: 3105
Epoch: [7]  [ 10/119]  eta: 0:01:12  lr: 0.000050  loss: 0.3997 (0.4045)  loss_classifier: 0.1250 (0.1259)  loss_box_reg: 0.1973 (0.2023)  loss_objectness: 0.0292 (0.0303)  loss_rpn_box_reg: 0.0479 (0.0459)  time: 0.6692  data: 0.1252  max mem: 3105
Epoch: [7]  [ 20/119]  eta: 0:01:07  lr: 0.000050  loss: 0.4224 (0.4219)  loss_classifier: 0.1250 (0.1262)  loss_box_reg: 0.2143 (0.2101)  loss_objectness: 0.0333 (0.0373)  loss_rpn_box_reg: 0.0493 (0.0484)  time: 0.6809  data: 0.1286  max mem: 3105
Epoch: [7]  [ 30/119]  eta: 0:01:00  lr: 0.000050  loss: 0.4120 (0.4140)  loss_classifier: 0.1191 (0.1221)  loss_box_reg: 0.2113 (0.2070)  loss_objectness: 0.0357 (0.0374)  loss_rpn_box_reg: 0.0506 (0.0475)  time: 0.6834  data: 0.1316  max mem: 3105
Epoch: [7]  [ 40/119]  eta: 0:00:53  lr: 0.000050  loss: 0.4013 (0.4117)  loss_classifier: 0.1118 (0.1210)  loss_box_reg: 0.2079 (0.2063)  loss_objectness: 0.0357 (0.0371)  loss_rpn_box_reg: 0.0471 (0.0473)  time: 0.6780  data: 0.1304  max mem: 3105
Epoch: [7]  [ 50/119]  eta: 0:00:46  lr: 0.000050  loss: 0.3911 (0.4035)  loss_classifier: 0.1172 (0.1198)  loss_box_reg: 0.1912 (0.2017)  loss_objectness: 0.0341 (0.0356)  loss_rpn_box_reg: 0.0449 (0.0464)  time: 0.6768  data: 0.1314  max mem: 3105
Epoch: [7]  [ 60/119]  eta: 0:00:39  lr: 0.000050  loss: 0.3911 (0.4048)  loss_classifier: 0.1186 (0.1213)  loss_box_reg: 0.1859 (0.2013)  loss_objectness: 0.0334 (0.0360)  loss_rpn_box_reg: 0.0412 (0.0462)  time: 0.6729  data: 0.1306  max mem: 3105
Epoch: [7]  [ 70/119]  eta: 0:00:33  lr: 0.000050  loss: 0.4046 (0.4030)  loss_classifier: 0.1177 (0.1209)  loss_box_reg: 0.2105 (0.2008)  loss_objectness: 0.0359 (0.0354)  loss_rpn_box_reg: 0.0462 (0.0459)  time: 0.6718  data: 0.1282  max mem: 3105
Epoch: [7]  [ 80/119]  eta: 0:00:26  lr: 0.000050  loss: 0.4125 (0.4067)  loss_classifier: 0.1187 (0.1221)  loss_box_reg: 0.2105 (0.2022)  loss_objectness: 0.0362 (0.0362)  loss_rpn_box_reg: 0.0469 (0.0462)  time: 0.6725  data: 0.1285  max mem: 3105
Epoch: [7]  [ 90/119]  eta: 0:00:19  lr: 0.000050  loss: 0.4289 (0.4068)  loss_classifier: 0.1288 (0.1223)  loss_box_reg: 0.2097 (0.2009)  loss_objectness: 0.0434 (0.0375)  loss_rpn_box_reg: 0.0479 (0.0461)  time: 0.6874  data: 0.1327  max mem: 3105
Epoch: [7]  [100/119]  eta: 0:00:12  lr: 0.000050  loss: 0.4222 (0.4086)  loss_classifier: 0.1223 (0.1221)  loss_box_reg: 0.2101 (0.2021)  loss_objectness: 0.0405 (0.0381)  loss_rpn_box_reg: 0.0483 (0.0463)  time: 0.6941  data: 0.1348  max mem: 3105
Epoch: [7]  [110/119]  eta: 0:00:06  lr: 0.000050  loss: 0.4082 (0.4072)  loss_classifier: 0.1196 (0.1220)  loss_box_reg: 0.2081 (0.2013)  loss_objectness: 0.0350 (0.0379)  loss_rpn_box_reg: 0.0475 (0.0461)  time: 0.6792  data: 0.1301  max mem: 3105
Epoch: [7]  [118/119]  eta: 0:00:00  lr: 0.000050  loss: 0.4070 (0.4076)  loss_classifier: 0.1196 (0.1223)  loss_box_reg: 0.2063 (0.2016)  loss_objectness: 0.0313 (0.0375)  loss_rpn_box_reg: 0.0475 (0.0462)  time: 0.6599  data: 0.1255  max mem: 3105
Epoch: [7] Total time: 0:01:20 (0.6763 s / it)
creating index...
index created!
Test:  [ 0/59]  eta: 0:00:14  model_time: 0.1194 (0.1194)  evaluator_time: 0.0633 (0.0633)  time: 0.2511  data: 0.0642  max mem: 3105
Test:  [58/59]  eta: 0:00:00  model_time: 0.1098 (0.1102)  evaluator_time: 0.0481 (0.0590)  time: 0.2353  data: 0.0625  max mem: 3105
Test: Total time: 0:00:13 (0.2371 s / it)
Averaged stats: model_time: 0.1098 (0.1102)  evaluator_time: 0.0481 (0.0590)
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.649
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.079
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.210
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.011
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.095
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.334
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

두 가지 질문이 있습니다.

  1. 과적 합 : 내 모델이 과적 합인지 과소 적합인지 모르겠습니다. 측정 항목을 찾는 방법은 무엇입니까?

  2. 모든 시대의 가장 좋은 모델 저장 : 다른 시대에 훈련 된 최상의 모델을 어떻게 저장할 수 있습니까? 결과에 따라 가장 좋은 시대는 무엇입니까?

감사합니다!

로마 인

테스트 데이터 세트 (또는 회수와 같은 다른 측정 항목)의 손실을 추적해야합니다. 이 코드 부분에주의를 기울이십시오.

for epoch in range(num_epochs):
    # train for one epoch, printing every 10 iterations
    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
    # update the learning rate
    lr_scheduler.step()
    # evaluate on the test dataset
    evaluate(model, data_loader_test, device=device)

train_one_epoch그리고 evaluate정의 여기 . Evaluate 함수는 유형의 객체를 반환 CocoEvaluator하지만 테스트 손실을 반환하도록 코드를 수정할 수 있습니다 ( CocoEvaluator어떻게 든 객체 에서 메트릭을 추출 하거나 자체 메트릭 평가를 작성해야 함).

따라서 대답은 다음과 같습니다.

  1. 테스트 손실을 추적하면 과적 합에 대해 알려줍니다.
  2. 테스트 손실이 증가하기 시작할 때까지 매 epoch 후 모델 상태를 저장합니다. 모델 저장에 대한 자습서는 여기에 있습니다 .

이 기사는 인터넷에서 수집됩니다. 재 인쇄 할 때 출처를 알려주십시오.

침해가 발생한 경우 연락 주시기 바랍니다[email protected] 삭제

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