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MAE-CT-M1N0-M12_v8_split1_v3

Video Classification
Transformers
Safetensors
videomae
Generated from Trainer
Inference Endpoints
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MAE-CT-M1N0-M12_v8_split1_v3 / README.md
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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: MAE-CT-M1N0-M12_v8_split1_v3
    results: []

MAE-CT-M1N0-M12_v8_split1_v3

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

  • Loss: 0.3263
  • Accuracy: 0.8696

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: 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: 10500

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6778 0.0068 71 0.6620 0.6622
0.6974 1.0068 142 0.6518 0.6622
0.7123 2.0068 213 0.6538 0.6622
0.6797 3.0068 284 0.6663 0.6757
0.6391 4.0068 355 0.6381 0.6622
0.643 5.0068 426 0.6440 0.6622
0.6763 6.0068 497 0.6331 0.6622
0.6547 7.0068 568 0.6475 0.6622
0.6751 8.0068 639 0.6370 0.6622
0.6847 9.0068 710 0.6344 0.6622
0.7185 10.0068 781 0.6262 0.6622
0.6961 11.0068 852 0.6510 0.6622
0.6824 12.0068 923 0.6236 0.7162
0.6169 13.0068 994 0.6485 0.6622
0.6172 14.0068 1065 0.5578 0.6622
0.6671 15.0068 1136 0.5988 0.6486
0.6063 16.0068 1207 0.5371 0.7027
0.4294 17.0068 1278 0.9391 0.6622
0.5702 18.0068 1349 0.5392 0.6757
0.5217 19.0068 1420 0.5673 0.6892
0.4067 20.0068 1491 0.6192 0.6892
0.2278 21.0068 1562 0.8934 0.6622
0.7341 22.0068 1633 0.6416 0.7027
0.4694 23.0068 1704 0.4830 0.7297
0.4655 24.0068 1775 0.8866 0.6757
0.433 25.0068 1846 0.8913 0.7568
0.4986 26.0068 1917 1.0156 0.7027
0.4063 27.0068 1988 1.1915 0.6892
0.3722 28.0068 2059 1.3529 0.6892
0.2947 29.0068 2130 1.6801 0.6351
0.1906 30.0068 2201 0.9845 0.6892
0.0161 31.0068 2272 1.0789 0.7027
0.5682 32.0068 2343 1.2568 0.7162
0.1105 33.0068 2414 1.0929 0.7432
0.1818 34.0068 2485 1.1917 0.7027
0.5396 35.0068 2556 1.4710 0.6892
0.0868 36.0068 2627 1.5799 0.7297
0.2748 37.0068 2698 1.3387 0.6892
0.1488 38.0068 2769 1.4294 0.6892
0.3124 39.0068 2840 1.1473 0.7027
0.1499 40.0068 2911 1.8165 0.6757
0.3149 41.0068 2982 2.0903 0.6351
0.02 42.0068 3053 1.9185 0.7027
0.0852 43.0068 3124 1.4491 0.6892
0.0115 44.0068 3195 1.6180 0.7297
0.5243 45.0068 3266 1.8516 0.6892
0.0658 46.0068 3337 1.6331 0.7027
0.1269 47.0068 3408 2.0585 0.6892
0.2941 48.0068 3479 2.1071 0.6892
0.2149 49.0068 3550 1.4238 0.7162
0.0017 50.0068 3621 1.6924 0.7297
0.0004 51.0068 3692 1.7705 0.7162
0.6701 52.0068 3763 2.1679 0.6892
0.5874 53.0068 3834 1.8656 0.6351
0.0004 54.0068 3905 2.1886 0.6622
0.0183 55.0068 3976 2.0148 0.6486
0.0056 56.0068 4047 1.9963 0.6892
0.0014 57.0068 4118 1.9338 0.7162
0.2153 58.0068 4189 1.6661 0.7297
0.0003 59.0068 4260 1.9540 0.7162
0.3193 60.0068 4331 2.1075 0.7027
0.0004 61.0068 4402 1.5376 0.7432
0.0003 62.0068 4473 1.9647 0.7027
0.0006 63.0068 4544 1.8878 0.7297
0.0018 64.0068 4615 1.7761 0.7297
0.0002 65.0068 4686 1.7536 0.7027
0.0001 66.0068 4757 2.2684 0.6757
0.0002 67.0068 4828 1.7061 0.7162
0.0498 68.0068 4899 1.8082 0.7162
0.0007 69.0068 4970 1.7665 0.7297
0.0019 70.0068 5041 2.5360 0.6757
0.0854 71.0068 5112 2.0176 0.6892
0.153 72.0068 5183 2.6058 0.6351
0.0001 73.0068 5254 1.9414 0.7162
0.1577 74.0068 5325 2.1872 0.6892
0.0001 75.0068 5396 1.9070 0.7027
0.0001 76.0068 5467 2.1586 0.7027
0.0001 77.0068 5538 2.4877 0.6757
0.0001 78.0068 5609 2.1836 0.7297
0.0021 79.0068 5680 2.6697 0.6622
0.0001 80.0068 5751 1.8825 0.7432
0.0004 81.0068 5822 2.1590 0.6892
0.0003 82.0068 5893 1.8814 0.7568
0.0118 83.0068 5964 1.8479 0.7027
0.1773 84.0068 6035 1.6983 0.7297
0.0025 85.0068 6106 2.5502 0.6351
0.0001 86.0068 6177 2.2446 0.7027
0.0001 87.0068 6248 2.0950 0.7162
0.0001 88.0068 6319 2.2134 0.7162
0.0001 89.0068 6390 1.9576 0.7432
0.0001 90.0068 6461 2.0430 0.7027
0.0001 91.0068 6532 2.1319 0.7297
0.0034 92.0068 6603 2.4718 0.6892
0.0001 93.0068 6674 2.5268 0.6892
0.0001 94.0068 6745 2.4211 0.7027
0.0001 95.0068 6816 2.3971 0.6892
0.1517 96.0068 6887 2.2035 0.7297
0.0001 97.0068 6958 2.3758 0.6757
0.0001 98.0068 7029 2.2253 0.7162
0.0001 99.0068 7100 2.3226 0.7162
0.0001 100.0068 7171 2.2541 0.7297
0.0 101.0068 7242 2.6355 0.6486
0.0 102.0068 7313 2.8393 0.6622
0.0001 103.0068 7384 2.1938 0.6892
0.0001 104.0068 7455 2.2225 0.7027
0.1038 105.0068 7526 2.4167 0.7027
0.0001 106.0068 7597 2.2465 0.7162
0.0001 107.0068 7668 2.4677 0.7027
0.0333 108.0068 7739 2.4546 0.6622
0.0119 109.0068 7810 2.5811 0.6892
0.0001 110.0068 7881 2.2874 0.7162
0.0 111.0068 7952 2.1970 0.7297
0.0 112.0068 8023 2.2009 0.7432
0.0001 113.0068 8094 2.2554 0.7432
0.0 114.0068 8165 2.2652 0.7162
0.0 115.0068 8236 2.3248 0.7162
0.0001 116.0068 8307 2.5589 0.6892
0.0 117.0068 8378 2.2266 0.7568
0.0 118.0068 8449 2.2807 0.6892
0.0 119.0068 8520 2.2664 0.7432
0.0 120.0068 8591 2.1452 0.7162
0.0001 121.0068 8662 2.2492 0.7297
0.0 122.0068 8733 2.2303 0.7432
0.0 123.0068 8804 2.2320 0.7432
0.0 124.0068 8875 2.2220 0.7162
0.0 125.0068 8946 2.2343 0.7027
0.0 126.0068 9017 2.3466 0.7162
0.0 127.0068 9088 2.4283 0.7027
0.0 128.0068 9159 2.3447 0.7162
0.0 129.0068 9230 2.7482 0.6892
0.0 130.0068 9301 2.4948 0.7297
0.0 131.0068 9372 2.5561 0.7027
0.0 132.0068 9443 2.4132 0.7162
0.0 133.0068 9514 2.3921 0.7297
0.0 134.0068 9585 2.3964 0.7297
0.0 135.0068 9656 2.5452 0.7027
0.0 136.0068 9727 2.5288 0.7027
0.0 137.0068 9798 2.4979 0.7162
0.0 138.0068 9869 2.4991 0.7162
0.0001 139.0068 9940 2.4993 0.7162
0.0 140.0068 10011 2.5002 0.7027
0.0 141.0068 10082 2.5028 0.7027
0.0 142.0068 10153 2.5063 0.7027
0.0 143.0068 10224 2.5081 0.7027
0.0 144.0068 10295 2.5087 0.7027
0.0 145.0068 10366 2.5091 0.7027
0.0 146.0068 10437 2.5093 0.7027
0.0 147.006 10500 2.5051 0.7027

Framework versions

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