videomae-base-finetuned-engine-subset-20230313

Para utilizar este modelo, puedes seguir los siguientes parámetros de hiperentrenamiento:

learning_rate: 5e-05
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam con betas=(0.9,0.999) y epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
training_steps: 1110

Resultados de entrenamiento:

Training Loss
Epoch
Step
Validation Loss
Accuracy
2.6212
0.03
38
2.3629
0.3774
2.455
1.03
76
2.3674
0.2170
2.4311
2.03
114
2.2191
0.3231
2.2768
3.03
152
2.1227
0.3608
1.7528
4.03
190
1.7296
0.4363
1.5381
5.03
228
1.5016
0.4340
1.407
6.03
266
1.2878
0.5448
1.1053
7.03
304
1.5210
0.4009
1.0893
8.03
342
1.3902
0.4623
0.8136
9.03
380
1.6456
0.4033
0.9565
10.03
418
1.1826
0.5613
1.0147
11.03
456
1.2099
0.5118
0.9125
12.03
494
1.1850
0.5495
0.7091
13.03
532
1.2324
0.5354
0.7361
14.03
570
1.0225
0.6226
0.6979
15.03
608
1.0738
0.5590
0.5265
16.03
646
1.1062
0.5873
0.5651
17.03
684
1.1402
0.5802
0.7182
18.03
722
1.0974
0.5802
0.6582
19.03
760
1.0529
0.6179
0.5709
20.03
798
0.9655
0.6344
0.4808
21.03
836
1.0441
0.6226
0.5816
22.03
874
0.9445
0.6439
0.5057
23.03
912
1.0248
0.6321
0.6253
24.03
950
0.9518
0.6604
0.6841
25.03
988
0.8913
0.6745
0.5933
26.03
1026
0.9013
0.6439
0.389
27.03
1064
0.9090
0.6627
0.3705
28.03
1102
0.8936
0.6722
0.6043
29.01
1110
0.8942
0.6722