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---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-ipm_all_videos_gb2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-ipm_all_videos_gb2
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5087
- Accuracy: 0.6957
## 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: 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: 9600
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.4413 | 0.01 | 60 | 2.5408 | 0.0696 |
| 2.3949 | 1.01 | 120 | 2.5420 | 0.0435 |
| 2.5429 | 2.01 | 180 | 2.5626 | 0.0957 |
| 2.4678 | 3.01 | 240 | 2.5721 | 0.0783 |
| 2.3535 | 4.01 | 300 | 2.5703 | 0.0783 |
| 2.3525 | 5.01 | 360 | 2.5966 | 0.0609 |
| 2.2312 | 6.01 | 420 | 2.3565 | 0.1913 |
| 2.0797 | 7.01 | 480 | 2.0738 | 0.1826 |
| 2.1423 | 8.01 | 540 | 2.0182 | 0.2435 |
| 1.8594 | 9.01 | 600 | 2.9555 | 0.0957 |
| 2.2635 | 10.01 | 660 | 2.1157 | 0.1565 |
| 2.0527 | 11.01 | 720 | 1.7646 | 0.2870 |
| 1.4499 | 12.01 | 780 | 2.2083 | 0.2696 |
| 1.3273 | 13.01 | 840 | 2.4202 | 0.2609 |
| 1.4349 | 14.01 | 900 | 1.9185 | 0.3043 |
| 1.476 | 15.01 | 960 | 2.1430 | 0.2261 |
| 1.2768 | 16.01 | 1020 | 1.6487 | 0.3391 |
| 1.2488 | 17.01 | 1080 | 1.7203 | 0.3130 |
| 1.5273 | 18.01 | 1140 | 1.9167 | 0.2783 |
| 1.6865 | 19.01 | 1200 | 2.1734 | 0.2522 |
| 1.448 | 20.01 | 1260 | 2.2406 | 0.3043 |
| 1.3169 | 21.01 | 1320 | 1.8596 | 0.2261 |
| 1.3004 | 22.01 | 1380 | 2.1954 | 0.2957 |
| 1.2201 | 23.01 | 1440 | 1.8007 | 0.3391 |
| 1.7577 | 24.01 | 1500 | 2.2078 | 0.2696 |
| 1.3741 | 25.01 | 1560 | 1.8426 | 0.3217 |
| 1.3676 | 26.01 | 1620 | 1.8888 | 0.3826 |
| 1.5892 | 27.01 | 1680 | 2.0376 | 0.3043 |
| 1.1962 | 28.01 | 1740 | 1.7738 | 0.3130 |
| 1.4768 | 29.01 | 1800 | 1.3115 | 0.4522 |
| 1.4112 | 30.01 | 1860 | 1.4297 | 0.3739 |
| 1.2148 | 31.01 | 1920 | 1.9232 | 0.2870 |
| 1.1125 | 32.01 | 1980 | 1.8406 | 0.3217 |
| 0.9814 | 33.01 | 2040 | 2.0529 | 0.3913 |
| 1.0787 | 34.01 | 2100 | 1.5659 | 0.3391 |
| 1.4073 | 35.01 | 2160 | 1.7671 | 0.3478 |
| 1.2131 | 36.01 | 2220 | 1.5678 | 0.3130 |
| 1.1894 | 37.01 | 2280 | 1.5435 | 0.4087 |
| 1.2001 | 38.01 | 2340 | 1.6149 | 0.3913 |
| 1.518 | 39.01 | 2400 | 1.7457 | 0.2957 |
| 1.1231 | 40.01 | 2460 | 1.7148 | 0.4 |
| 0.9362 | 41.01 | 2520 | 1.5611 | 0.4174 |
| 1.1348 | 42.01 | 2580 | 1.2901 | 0.3826 |
| 0.9504 | 43.01 | 2640 | 1.4024 | 0.4 |
| 1.2008 | 44.01 | 2700 | 1.6685 | 0.4609 |
| 1.0468 | 45.01 | 2760 | 1.6202 | 0.4174 |
| 0.7304 | 46.01 | 2820 | 1.4007 | 0.4522 |
| 0.8522 | 47.01 | 2880 | 1.5439 | 0.4174 |
| 0.9106 | 48.01 | 2940 | 1.6536 | 0.4783 |
| 0.7837 | 49.01 | 3000 | 1.4113 | 0.4609 |
| 0.6869 | 50.01 | 3060 | 1.2071 | 0.5391 |
| 0.8787 | 51.01 | 3120 | 1.3023 | 0.5130 |
| 0.8072 | 52.01 | 3180 | 1.2058 | 0.6 |
| 0.9491 | 53.01 | 3240 | 1.5370 | 0.4957 |
| 0.7642 | 54.01 | 3300 | 1.2301 | 0.5652 |
| 0.6676 | 55.01 | 3360 | 1.4549 | 0.5391 |
| 0.8502 | 56.01 | 3420 | 1.6117 | 0.4522 |
| 1.0006 | 57.01 | 3480 | 1.3982 | 0.4957 |
| 0.8304 | 58.01 | 3540 | 1.3233 | 0.4783 |
| 0.9832 | 59.01 | 3600 | 1.2982 | 0.5478 |
| 0.3973 | 60.01 | 3660 | 1.3903 | 0.5478 |
| 0.9487 | 61.01 | 3720 | 1.4241 | 0.5304 |
| 0.9319 | 62.01 | 3780 | 1.4913 | 0.5565 |
| 0.6713 | 63.01 | 3840 | 1.4731 | 0.5826 |
| 0.7139 | 64.01 | 3900 | 1.0942 | 0.6870 |
| 0.7852 | 65.01 | 3960 | 1.2570 | 0.6348 |
| 1.0018 | 66.01 | 4020 | 1.1249 | 0.5913 |
| 0.7371 | 67.01 | 4080 | 1.4665 | 0.5565 |
| 0.6106 | 68.01 | 4140 | 1.7390 | 0.4957 |
| 0.8815 | 69.01 | 4200 | 1.5044 | 0.5652 |
| 0.6724 | 70.01 | 4260 | 1.8060 | 0.4957 |
| 0.5907 | 71.01 | 4320 | 1.5552 | 0.5391 |
| 0.6218 | 72.01 | 4380 | 1.6037 | 0.5826 |
| 0.7698 | 73.01 | 4440 | 1.4280 | 0.5913 |
| 0.6719 | 74.01 | 4500 | 1.6870 | 0.5565 |
| 0.3956 | 75.01 | 4560 | 1.6326 | 0.5217 |
| 0.6272 | 76.01 | 4620 | 1.3282 | 0.6 |
| 0.4354 | 77.01 | 4680 | 1.5181 | 0.5913 |
| 0.8649 | 78.01 | 4740 | 1.4137 | 0.5913 |
| 0.48 | 79.01 | 4800 | 1.6439 | 0.5913 |
| 0.9693 | 80.01 | 4860 | 1.6453 | 0.5739 |
| 0.3872 | 81.01 | 4920 | 1.5209 | 0.6696 |
| 0.913 | 82.01 | 4980 | 1.5002 | 0.6435 |
| 0.7185 | 83.01 | 5040 | 1.8319 | 0.5478 |
| 1.0149 | 84.01 | 5100 | 1.5270 | 0.5826 |
| 0.3811 | 85.01 | 5160 | 1.3813 | 0.6609 |
| 0.4902 | 86.01 | 5220 | 1.3160 | 0.6348 |
| 1.2717 | 87.01 | 5280 | 1.5052 | 0.6696 |
| 0.5379 | 88.01 | 5340 | 1.4357 | 0.6870 |
| 0.7101 | 89.01 | 5400 | 1.7699 | 0.5739 |
| 0.6517 | 90.01 | 5460 | 1.3428 | 0.6609 |
| 0.6213 | 91.01 | 5520 | 1.4725 | 0.6087 |
| 0.6995 | 92.01 | 5580 | 1.2645 | 0.6435 |
| 0.3997 | 93.01 | 5640 | 1.5827 | 0.5652 |
| 0.7778 | 94.01 | 5700 | 1.2344 | 0.7304 |
| 0.5093 | 95.01 | 5760 | 1.2908 | 0.6957 |
| 0.6022 | 96.01 | 5820 | 1.3528 | 0.6609 |
| 0.508 | 97.01 | 5880 | 1.4460 | 0.6783 |
| 0.4772 | 98.01 | 5940 | 1.1836 | 0.7478 |
| 0.8776 | 99.01 | 6000 | 1.4956 | 0.6435 |
| 0.7514 | 100.01 | 6060 | 1.4904 | 0.6609 |
| 0.1734 | 101.01 | 6120 | 1.6757 | 0.6087 |
| 0.5279 | 102.01 | 6180 | 1.8148 | 0.5913 |
| 0.2101 | 103.01 | 6240 | 1.4176 | 0.6348 |
| 0.6081 | 104.01 | 6300 | 1.7604 | 0.5913 |
| 0.2781 | 105.01 | 6360 | 1.7557 | 0.6087 |
| 0.2321 | 106.01 | 6420 | 1.3726 | 0.6696 |
| 0.4503 | 107.01 | 6480 | 1.6582 | 0.6348 |
| 0.4361 | 108.01 | 6540 | 2.0009 | 0.5913 |
| 0.4934 | 109.01 | 6600 | 1.9722 | 0.5217 |
| 0.3898 | 110.01 | 6660 | 1.5016 | 0.6696 |
| 0.4286 | 111.01 | 6720 | 1.5307 | 0.6783 |
| 0.2792 | 112.01 | 6780 | 1.5770 | 0.6696 |
| 0.2254 | 113.01 | 6840 | 1.7076 | 0.6522 |
| 0.1739 | 114.01 | 6900 | 2.0225 | 0.5826 |
| 0.1951 | 115.01 | 6960 | 1.8448 | 0.6174 |
| 0.614 | 116.01 | 7020 | 1.5507 | 0.6696 |
| 0.6894 | 117.01 | 7080 | 1.5430 | 0.6609 |
| 0.9059 | 118.01 | 7140 | 1.6563 | 0.6696 |
| 0.4592 | 119.01 | 7200 | 1.5566 | 0.7043 |
| 0.3895 | 120.01 | 7260 | 1.5251 | 0.7130 |
| 0.4897 | 121.01 | 7320 | 1.7417 | 0.6696 |
| 0.5362 | 122.01 | 7380 | 1.5845 | 0.6783 |
| 0.4484 | 123.01 | 7440 | 1.6405 | 0.6870 |
| 0.557 | 124.01 | 7500 | 1.5133 | 0.7130 |
| 0.4878 | 125.01 | 7560 | 1.3845 | 0.7391 |
| 0.2704 | 126.01 | 7620 | 1.4704 | 0.6957 |
| 0.7636 | 127.01 | 7680 | 1.4413 | 0.6957 |
| 0.4196 | 128.01 | 7740 | 1.4106 | 0.7043 |
| 0.5835 | 129.01 | 7800 | 1.2571 | 0.7391 |
| 0.6156 | 130.01 | 7860 | 1.8000 | 0.6609 |
| 0.3074 | 131.01 | 7920 | 1.7324 | 0.6435 |
| 0.4697 | 132.01 | 7980 | 1.5218 | 0.7043 |
| 0.2968 | 133.01 | 8040 | 1.3640 | 0.7391 |
| 0.452 | 134.01 | 8100 | 1.4916 | 0.7217 |
| 0.2699 | 135.01 | 8160 | 1.6554 | 0.6957 |
| 0.3889 | 136.01 | 8220 | 1.5015 | 0.7391 |
| 0.5006 | 137.01 | 8280 | 1.4134 | 0.7391 |
| 0.135 | 138.01 | 8340 | 1.3987 | 0.7565 |
| 0.3882 | 139.01 | 8400 | 1.4364 | 0.7304 |
| 0.194 | 140.01 | 8460 | 1.6716 | 0.6957 |
| 0.1185 | 141.01 | 8520 | 1.8543 | 0.6609 |
| 0.4103 | 142.01 | 8580 | 1.9628 | 0.6348 |
| 0.1577 | 143.01 | 8640 | 1.7975 | 0.6609 |
| 0.2213 | 144.01 | 8700 | 1.6324 | 0.6870 |
| 0.6129 | 145.01 | 8760 | 1.5654 | 0.7130 |
| 0.54 | 146.01 | 8820 | 1.4210 | 0.7565 |
| 0.357 | 147.01 | 8880 | 1.4255 | 0.7478 |
| 0.2451 | 148.01 | 8940 | 1.6774 | 0.6957 |
| 0.4752 | 149.01 | 9000 | 1.7326 | 0.6957 |
| 0.1847 | 150.01 | 9060 | 1.7124 | 0.6609 |
| 0.2618 | 151.01 | 9120 | 1.6317 | 0.6783 |
| 0.4884 | 152.01 | 9180 | 1.6136 | 0.6870 |
| 0.4929 | 153.01 | 9240 | 1.5062 | 0.7217 |
| 0.5781 | 154.01 | 9300 | 1.4666 | 0.7217 |
| 0.4633 | 155.01 | 9360 | 1.5033 | 0.7043 |
| 0.5355 | 156.01 | 9420 | 1.4821 | 0.6957 |
| 0.551 | 157.01 | 9480 | 1.4866 | 0.6957 |
| 0.3247 | 158.01 | 9540 | 1.5070 | 0.6957 |
| 0.5455 | 159.01 | 9600 | 1.5087 | 0.6957 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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