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steveice
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videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen

Video Classification
Transformers
PyTorch
videomae
Generated from Trainer
Inference Endpoints
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videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen / README.md
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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen
    results: []

videomae-large-finetuned-kinetics-finetuned-videomae-large-kitchen

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

  • Loss: 0.6309
  • Accuracy: 0.8900

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: 2
  • eval_batch_size: 2
  • 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: 11100

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.5158 0.02 222 3.6067 0.0588
2.8571 1.02 444 3.1445 0.3014
1.8854 2.02 666 2.3644 0.4607
1.5533 3.02 888 1.7967 0.5621
1.3935 4.02 1110 1.3755 0.6502
1.1722 5.02 1332 1.2232 0.7109
0.2896 6.02 1554 1.2859 0.6256
0.3166 7.02 1776 1.2910 0.6720
0.6902 8.02 1998 1.2702 0.6995
0.4193 9.02 2220 1.2087 0.7137
0.1889 10.02 2442 1.0500 0.7611
0.4502 11.02 2664 1.1647 0.7118
0.7703 12.02 2886 1.1037 0.7242
0.0957 13.02 3108 1.0967 0.7706
0.3202 14.02 3330 1.0479 0.7545
0.3634 15.02 3552 1.0714 0.8057
0.3883 16.02 3774 1.2323 0.7498
0.0322 17.02 3996 1.0504 0.7848
0.5108 18.02 4218 1.1356 0.7915
0.309 19.02 4440 1.1409 0.7592
0.56 20.02 4662 1.0828 0.7915
0.3675 21.02 4884 0.9154 0.8123
0.0076 22.02 5106 1.0974 0.8133
0.0451 23.02 5328 1.0361 0.8152
0.2558 24.02 5550 0.7830 0.8237
0.0125 25.02 5772 0.8728 0.8171
0.4184 26.02 5994 0.8413 0.8265
0.2566 27.02 6216 1.0644 0.8009
0.1257 28.02 6438 0.8641 0.8265
0.1326 29.02 6660 0.8444 0.8417
0.0436 30.02 6882 0.8615 0.8322
0.0408 31.02 7104 0.8075 0.8332
0.0316 32.02 7326 0.8699 0.8341
0.2235 33.02 7548 0.8151 0.8455
0.0079 34.02 7770 0.8099 0.8550
0.001 35.02 7992 0.8640 0.8370
0.0007 36.02 8214 0.7146 0.8483
0.464 37.02 8436 0.7917 0.8464
0.0005 38.02 8658 0.7239 0.8531
0.0004 39.02 8880 0.7702 0.8701
0.1705 40.02 9102 0.7543 0.8521
0.0039 41.02 9324 0.7456 0.8673
0.0168 42.02 9546 0.7255 0.8730
0.2615 43.02 9768 0.7453 0.8758
0.0004 44.02 9990 0.6824 0.8806
0.236 45.02 10212 0.6624 0.8825
0.0007 46.02 10434 0.6727 0.8815
0.0004 47.02 10656 0.6478 0.8863
0.268 48.02 10878 0.6309 0.8900
0.0025 49.02 11100 0.6284 0.8900

Framework versions

  • Transformers 4.33.2
  • Pytorch 1.12.1+cu113
  • Datasets 2.14.5
  • Tokenizers 0.13.3