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MAE-CT-CPC-Dicotomized-v3

Safetensors
videomae
Generated from Trainer
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MAE-CT-CPC-Dicotomized-v3 / 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: MAE-CT-CPC-Dicotomized-v3
    results: []

MAE-CT-CPC-Dicotomized-v3

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: 0.8472
  • Accuracy: 0.7317

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: 4
  • eval_batch_size: 4
  • 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: 4000

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5652 0.02 81 0.6260 0.6829
0.6109 1.02 162 0.6052 0.6829
0.7142 2.02 243 0.6207 0.6829
0.5037 3.02 324 0.6219 0.6829
0.5177 4.02 405 0.6456 0.6829
0.5363 5.02 486 0.5791 0.6829
0.6797 6.02 567 0.6295 0.6829
0.557 7.02 648 0.5182 0.7073
0.5645 8.02 729 0.4608 0.7073
0.4484 9.02 810 0.4721 0.7805
0.3354 10.02 891 0.7011 0.7317
0.2875 11.02 972 0.4498 0.7805
0.3535 12.02 1053 0.8161 0.7561
0.294 13.02 1134 0.5175 0.7805
0.1907 14.02 1215 0.3750 0.8537
0.2533 15.02 1296 0.6400 0.7805
0.2499 16.02 1377 0.9233 0.7805
0.2726 17.02 1458 1.0686 0.7805
0.4447 18.02 1539 1.0433 0.7073
0.1554 19.02 1620 1.0791 0.8293
0.0163 20.02 1701 1.0939 0.7317
0.0507 21.02 1782 1.1601 0.8049
0.0116 22.02 1863 1.3358 0.7073
0.015 23.02 1944 1.0973 0.8049
0.1993 24.02 2025 1.2985 0.7805
0.1496 25.02 2106 1.1559 0.7805
0.2059 26.02 2187 1.5124 0.7561
0.0176 27.02 2268 1.4691 0.7805
0.2477 28.02 2349 1.5908 0.7805
0.0007 29.02 2430 1.3929 0.7805
0.0003 30.02 2511 1.3020 0.7805
0.0002 31.02 2592 1.5539 0.7805
0.0001 32.02 2673 1.5173 0.8049
0.0001 33.02 2754 1.6113 0.8049
0.0918 34.02 2835 1.5837 0.7805
0.0001 35.02 2916 1.4928 0.8049
0.0001 36.02 2997 1.4138 0.8049
0.2197 37.02 3078 1.3980 0.8049
0.0001 38.02 3159 1.6004 0.8049
0.0002 39.02 3240 1.5590 0.8049
0.0246 40.02 3321 1.5889 0.7805
0.0002 41.02 3402 1.6164 0.7805
0.0001 42.02 3483 1.6072 0.7805
0.0001 43.02 3564 1.6259 0.7805
0.0001 44.02 3645 1.5990 0.7805
0.0002 45.02 3726 1.5517 0.7805
0.0 46.02 3807 1.5260 0.7805
0.0146 47.02 3888 1.5292 0.7805
0.0001 48.02 3969 1.5405 0.7805
0.0001 49.01 4000 1.5414 0.7805

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.0.1+cu117
  • Datasets 3.0.1
  • Tokenizers 0.15.1