videomae-base-finetuned-ucf101-subset

Se usaron los siguientes hiperparámetros durante el entrenamiento:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: 600

Resultados del entrenamiento:

Pérdida de Entrenamiento
Época
Paso
Pérdida de Validación
Precisión

2.2101
0.0633
38
2.1049
0.3143

1.286
1.0633
76
1.1230
0.5286

0.4989
2.0633
114
0.4172
0.8714

0.4114
3.0633
152
0.3117
0.8286

0.1907
4.0633
190
0.2904
0.9

0.1088
5.0633
228
0.1609
0.9571

0.057
6.0633
266
0.0535
0.9857

0.117
7.0633
304
0.2749
0.9143

0.1324
8.0633
342
0.1784
0.9429

0.0081
9.0633
380
0.0184
1.0

0.008
10.0633
418
0.0355
0.9857

0.0038
11.0633
456
0.0087
1.0

0.0033
12.0633
494
0.0086
1.0

0.0033
13.0633
532
0.0506
0.9857

0.0035
14.0633
570
0.0357
0.9857

0.0031
15.05
600
0.0365
0.9857