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CTMAE-P2-V3-3G-S5

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
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CTMAE-P2-V3-3G-S5 / README.md
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: CTMAE-P2-V3-3G-S5
    results: []

CTMAE-P2-V3-3G-S5

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: 0.7553
  • Accuracy: 0.8409

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 6500

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6815 0.0202 131 0.7046 0.5227
0.4922 1.0202 262 0.9263 0.5227
0.6484 2.0202 393 0.7719 0.5227
0.6707 3.0202 524 0.7503 0.5227
1.1086 4.0202 655 0.7398 0.5227
0.6679 5.0202 786 0.6244 0.5227
0.7521 6.0202 917 0.7773 0.5227
1.7822 7.0202 1048 1.4883 0.5227
0.7714 8.0202 1179 1.0682 0.5455
0.5244 9.0202 1310 0.7003 0.5227
0.7346 10.0202 1441 1.0285 0.5
0.4019 11.0202 1572 0.6361 0.6364
0.7769 12.0202 1703 0.4666 0.7727
0.6358 13.0202 1834 1.3155 0.6364
0.5129 14.0202 1965 1.1485 0.6818
1.1003 15.0202 2096 0.5102 0.7955
0.7943 16.0202 2227 0.6434 0.7273
0.363 17.0202 2358 0.9532 0.7955
0.2225 18.0202 2489 0.6883 0.7273
0.2107 19.0202 2620 0.6559 0.8182
1.0048 20.0202 2751 0.9784 0.7727
0.4285 21.0202 2882 0.9721 0.7273
0.0459 22.0202 3013 0.6200 0.8182
0.9143 23.0202 3144 1.4102 0.6818
0.426 24.0202 3275 1.4732 0.6591
0.8278 25.0202 3406 1.0037 0.7955
0.7064 26.0202 3537 0.7553 0.8409
0.6217 27.0202 3668 1.5788 0.7045
0.6104 28.0202 3799 1.4799 0.7045
0.4625 29.0202 3930 2.0381 0.6136
0.1935 30.0202 4061 1.8624 0.6136
0.3657 31.0202 4192 1.4570 0.7273
0.0952 32.0202 4323 1.2123 0.8182
0.6802 33.0202 4454 1.1155 0.8409
0.1602 34.0202 4585 1.0990 0.8182
0.269 35.0202 4716 1.3373 0.7727
0.2087 36.0202 4847 1.0545 0.7955
0.4447 37.0202 4978 1.1182 0.7955
0.803 38.0202 5109 1.9385 0.7045
0.685 39.0202 5240 1.5079 0.75
0.001 40.0202 5371 1.3848 0.7955
0.7371 41.0202 5502 1.8426 0.7273
0.0008 42.0202 5633 1.9710 0.7273
0.2964 43.0202 5764 2.2087 0.7045
0.0008 44.0202 5895 1.5878 0.7727
0.0005 45.0202 6026 1.8426 0.7273
0.4393 46.0202 6157 1.7193 0.75
0.0036 47.0202 6288 1.9741 0.7273
0.0005 48.0202 6419 1.8383 0.75
0.1776 49.0125 6500 1.8362 0.75

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.0.1+cu117
  • Datasets 3.0.1
  • Tokenizers 0.20.0