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vivit-b-16x2-kinetics400-ft-3620

Video Classification
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vivit
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vivit-b-16x2-kinetics400-ft-3620 / README.md
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metadata
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vivit-b-16x2-kinetics400-ft-3620
    results: []

vivit-b-16x2-kinetics400-ft-3620

This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9281
  • Accuracy: 0.5566

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: 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: 5500

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0684 0.0202 111 1.1114 0.3799
1.0415 1.0202 222 1.0135 0.5249
1.0271 2.0202 333 1.0630 0.4857
1.1609 3.0202 444 1.0203 0.4222
0.9824 4.0202 555 1.0219 0.5249
1.0247 5.0202 666 1.0210 0.5026
1.0824 6.0202 777 0.9947 0.4720
0.943 7.0202 888 1.1085 0.4381
0.8807 8.0202 999 0.9345 0.5767
1.1009 9.0202 1110 0.9855 0.5164
1.0292 10.0202 1221 1.0506 0.4339
0.9071 11.0202 1332 0.9926 0.5143
1.0001 12.0202 1443 1.0406 0.4931
0.9698 13.0202 1554 0.9440 0.5598
0.9405 14.0202 1665 0.9667 0.5323
0.8802 15.0202 1776 0.9011 0.5862
0.9154 16.0202 1887 0.9429 0.5598
0.929 17.0202 1998 0.9948 0.5132
0.9112 18.0202 2109 0.9056 0.5852
0.9202 19.0202 2220 0.9489 0.5524
0.9004 20.0202 2331 0.8995 0.5820
0.9318 21.0202 2442 0.9032 0.5958
0.8493 22.0202 2553 0.9975 0.5238
0.8587 23.0202 2664 1.0142 0.5259
0.958 24.0202 2775 0.9665 0.5376
0.996 25.0202 2886 0.9391 0.5704
0.823 26.0202 2997 0.9171 0.5778
0.8834 27.0202 3108 0.8923 0.5873
0.8615 28.0202 3219 0.9577 0.5471
0.9462 29.0202 3330 0.9468 0.5630
0.8909 30.0202 3441 0.9343 0.5672
0.8048 31.0202 3552 0.9107 0.5778
0.8109 32.0202 3663 0.9547 0.5492
0.9242 33.0202 3774 0.9275 0.5598
0.9046 34.0202 3885 0.9290 0.5831
0.7677 35.0202 3996 0.9208 0.5725
0.8501 36.0202 4107 0.9126 0.5810
0.8468 37.0202 4218 0.9053 0.5862
0.7814 38.0202 4329 0.8858 0.5905
0.9354 39.0202 4440 0.9207 0.5725
0.8849 40.0202 4551 0.9277 0.5651
0.7856 41.0202 4662 0.9130 0.5915
0.7133 42.0202 4773 0.9080 0.5884
0.932 43.0202 4884 0.9388 0.5577
0.6883 44.0202 4995 0.8925 0.5937
0.9944 45.0202 5106 0.9143 0.5820
0.8892 46.0202 5217 0.9103 0.5884
0.9071 47.0202 5328 0.9018 0.5905
0.7943 48.0202 5439 0.9022 0.5905
0.8034 49.0111 5500 0.9004 0.5947

Framework versions

  • Transformers 4.41.2
  • Pytorch 1.13.0+cu117
  • Datasets 2.20.0
  • Tokenizers 0.19.1