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videomae-base-finetuned-ucf101-subset

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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-finetuned-ucf101-subset
    results: []

videomae-base-finetuned-ucf101-subset

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

  • Loss: 0.7149
  • Accuracy: 0.9038

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 15000

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.0174 0.02 300 0.2750 0.9423
0.0218 1.02 600 0.7020 0.8269
0.5348 2.02 900 1.1836 0.7692
0.9667 3.02 1200 1.9316 0.5962
2.0242 4.02 1500 1.5680 0.6923
0.7852 5.02 1800 0.5868 0.9038
1.9104 6.02 2100 1.8121 0.7115
1.1466 7.02 2400 1.3801 0.75
0.0025 8.02 2700 1.2799 0.7692
0.0005 9.02 3000 1.8073 0.7115
0.0005 10.02 3300 0.4820 0.9231
0.5816 11.02 3600 0.7625 0.8846
0.0014 12.02 3900 0.4762 0.9231
0.0694 13.02 4200 1.3250 0.8077
0.0002 14.02 4500 0.9637 0.8654
0.0003 15.02 4800 0.4808 0.9231
0.0003 16.02 5100 0.8623 0.8846
0.0002 17.02 5400 0.6881 0.9231
0.0004 18.02 5700 0.5577 0.9038
0.0001 19.02 6000 0.5069 0.9231
0.4994 20.02 6300 0.3667 0.9423
0.0002 21.02 6600 0.3666 0.9423
1.0279 22.02 6900 1.0781 0.8654
0.0135 23.02 7200 2.2670 0.7308
0.0002 24.02 7500 0.1732 0.9615
0.0002 25.02 7800 0.4422 0.9423
0.0001 26.02 8100 0.8196 0.8846
0.0001 27.02 8400 0.8037 0.8846
0.0001 28.02 8700 0.8696 0.8846
0.0002 29.02 9000 0.7887 0.9231
0.7745 30.02 9300 0.3868 0.9423
0.0001 31.02 9600 0.4386 0.9423
0.0002 32.02 9900 0.4036 0.9423
0.0001 33.02 10200 0.3513 0.9423
0.0001 34.02 10500 0.3075 0.9423
0.0001 35.02 10800 0.5712 0.9231
0.0005 36.02 11100 0.6482 0.9231
0.0001 37.02 11400 0.8843 0.9038
0.0001 38.02 11700 0.9147 0.8846
0.0001 39.02 12000 0.6891 0.9038
0.0001 40.02 12300 0.8976 0.8846
0.0001 41.02 12600 1.6405 0.8077
0.0001 42.02 12900 1.0550 0.8654
0.0 43.02 13200 1.0356 0.8654
0.0 44.02 13500 1.0037 0.8462
0.0 45.02 13800 0.9632 0.8654
0.0 46.02 14100 0.6649 0.9231
0.0 47.02 14400 0.8702 0.8846
0.0 48.02 14700 0.7201 0.9038
0.0 49.02 15000 0.7149 0.9038

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1