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MAE-CT-CPC-Dicotomized-v8-n0-m1

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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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: MAE-CT-CPC-Dicotomized-v8-n0-m1
    results: []

MAE-CT-CPC-Dicotomized-v8-n0-m1

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: 2.5946
  • Accuracy: 0.5753

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: 2500

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6826 0.0204 51 0.6157 0.7625
0.6549 1.0204 102 0.5930 0.7625
0.6486 2.0204 153 0.6797 0.5125
0.5595 3.0204 204 0.4902 0.6625
0.5586 4.0204 255 0.8195 0.475
0.4565 5.0204 306 0.5872 0.75
0.3697 6.0204 357 0.5017 0.775
0.6201 7.0204 408 0.6555 0.7
0.4333 8.0204 459 1.2277 0.6125
0.2148 9.0204 510 0.8115 0.7625
0.9458 10.0204 561 0.9873 0.6625
0.0651 11.0204 612 1.0840 0.7625
0.5756 12.0204 663 1.0489 0.8
0.354 13.0204 714 1.1601 0.7875
0.2888 14.0204 765 1.8144 0.625
0.2449 15.0204 816 1.3988 0.7125
0.1326 16.0204 867 1.7152 0.7125
0.0018 17.0204 918 2.1475 0.6375
0.3631 18.0204 969 1.8957 0.65
0.1252 19.0204 1020 1.1246 0.825
0.0943 20.0204 1071 1.9498 0.6625
0.3488 21.0204 1122 1.3457 0.7875
0.0008 22.0204 1173 1.7872 0.7125
0.009 23.0204 1224 1.5437 0.75
0.0274 24.0204 1275 1.9865 0.6875
0.0004 25.0204 1326 1.5100 0.7625
0.1007 26.0204 1377 1.9590 0.6875
0.0006 27.0204 1428 1.8346 0.7125
0.0006 28.0204 1479 1.4669 0.825
0.0001 29.0204 1530 1.5396 0.7875
0.0002 30.0204 1581 1.5716 0.7875
0.0001 31.0204 1632 1.6614 0.7625
0.0002 32.0204 1683 1.6356 0.7625
0.0001 33.0204 1734 1.5731 0.8
0.0001 34.0204 1785 2.0020 0.725
0.0001 35.0204 1836 1.8886 0.75
0.0001 36.0204 1887 1.8363 0.75
0.0001 37.0204 1938 1.6848 0.7625
0.0001 38.0204 1989 1.7188 0.75
0.0001 39.0204 2040 1.5820 0.8
0.0001 40.0204 2091 1.6061 0.7875
0.0001 41.0204 2142 2.2817 0.7
0.0001 42.0204 2193 2.1015 0.725
0.0001 43.0204 2244 1.6356 0.775
0.0001 44.0204 2295 1.5849 0.8125
0.0001 45.0204 2346 1.6463 0.775
0.0001 46.0204 2397 1.6641 0.775
0.0001 47.0204 2448 1.6123 0.7875
0.0001 48.0204 2499 1.6145 0.7875
0.0001 49.0004 2500 1.6145 0.7875

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.0.1+cu117
  • Datasets 3.0.1
  • Tokenizers 0.20.0