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videomae-base-finetuned-subset-check10

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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videomae-base-finetuned-subset-check10 / README.md
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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-subset-check10
    results: []

videomae-base-finetuned-subset-check10

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: 0.8682
  • Accuracy: 0.6343

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • 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: 2220

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.5175 0.03 56 1.6041 0.2074
1.4397 1.03 112 1.4559 0.3871
1.464 2.03 168 1.3637 0.3963
1.3404 3.03 224 1.2467 0.4470
1.3284 4.03 280 1.3115 0.3318
1.1598 5.03 336 1.2489 0.4470
0.9615 6.03 392 1.3057 0.4009
0.9357 7.03 448 0.9201 0.6498
0.9785 8.03 504 0.8629 0.6774
1.0862 9.03 560 1.0977 0.5069
0.9315 10.03 616 0.7868 0.7097
0.9404 11.03 672 0.8170 0.6728
0.939 12.03 728 0.9246 0.6636
0.8205 13.03 784 0.8420 0.6866
0.6719 14.03 840 1.0725 0.5899
0.8308 15.03 896 0.8683 0.6912
0.7554 16.03 952 0.9684 0.5991
0.6962 17.03 1008 1.1106 0.5484
0.7995 18.03 1064 0.9751 0.6498
0.8298 19.03 1120 1.0631 0.5300
0.6607 20.03 1176 0.9458 0.6175
0.688 21.03 1232 1.0296 0.6037
0.5835 22.03 1288 0.8948 0.6774
0.6987 23.03 1344 0.7883 0.7189
0.4979 24.03 1400 0.7089 0.7189
0.6163 25.03 1456 0.7634 0.7235
0.6754 26.03 1512 0.9444 0.6359
0.6673 27.03 1568 0.8391 0.6544
0.4924 28.03 1624 0.8289 0.6682
0.6438 29.03 1680 0.9605 0.6129
0.5714 30.03 1736 0.8838 0.6452
0.6726 31.03 1792 0.8412 0.6590
0.5027 32.03 1848 0.8439 0.6728
0.4649 33.03 1904 0.9525 0.6267
0.6625 34.03 1960 0.7850 0.7281
0.5793 35.03 2016 0.8481 0.6728
0.6411 36.03 2072 0.8842 0.6590
0.6592 37.03 2128 0.8028 0.6912
0.5524 38.03 2184 0.8216 0.6866
0.5697 39.02 2220 0.8339 0.6774

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.1
  • Datasets 2.16.1
  • Tokenizers 0.15.0