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videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel-4

This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4886
  • F1: 0.8863
  • Roc Auc: 0.9209
  • Accuracy: 0.7962

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1.5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 50000

Training results

Training Loss Epoch Step Validation Loss F1 Roc Auc Accuracy
0.771 0.04 2000 0.7660 0.6227 0.7324 0.2937
0.6225 1.04 4000 0.6446 0.7401 0.8162 0.4653
0.6093 2.04 6000 0.6517 0.7441 0.8223 0.5253
0.603 3.04 8000 0.5630 0.8151 0.8722 0.6558
0.6547 4.04 10000 0.5009 0.8425 0.8911 0.6695
0.6426 5.04 12000 0.5179 0.8422 0.9012 0.6642
0.4447 6.04 14000 0.5052 0.8537 0.8973 0.7147
0.6949 7.04 16000 0.5045 0.8548 0.8974 0.7337
0.509 8.04 18000 0.5262 0.8705 0.9089 0.7768
0.4341 9.04 20000 0.4731 0.8831 0.9163 0.7863
0.5037 10.04 22000 0.5040 0.8781 0.9154 0.7747
0.3592 11.04 24000 0.5091 0.8746 0.9101 0.7821
0.2829 12.04 26000 0.4709 0.8867 0.9230 0.7905
0.3599 13.04 28000 0.4722 0.8888 0.9230 0.7947
0.4152 14.04 30000 0.4744 0.8911 0.9247 0.8074
0.1167 15.04 32000 0.4817 0.8949 0.9241 0.8179
0.2721 16.04 34000 0.4627 0.9064 0.9337 0.8274
0.42 17.04 36000 0.4849 0.8992 0.9282 0.8295
0.5841 18.04 38000 0.4747 0.9026 0.9304 0.8379
0.509 19.04 40000 0.4779 0.8991 0.9273 0.8316
0.1599 20.04 42000 0.4945 0.9056 0.9319 0.8411
0.3645 21.04 44000 0.4875 0.8984 0.9277 0.8242
0.4402 22.04 46000 0.5156 0.9004 0.9283 0.8368
0.2769 23.04 48000 0.5063 0.9028 0.9296 0.8389
0.3009 24.04 50000 0.5122 0.8975 0.9257 0.8326

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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