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# videomae-base-finetuned-subset-100epochs

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: 0.7077
- Accuracy: 0.7685

## 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: 8
- eval_batch_size: 8
- 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: 5550

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6657        | 0.01  | 56   | 1.6248          | 0.2258   |
| 1.6109        | 1.01  | 112  | 1.5601          | 0.3917   |
| 1.5669        | 2.01  | 168  | 1.5563          | 0.3733   |
| 1.45          | 3.01  | 224  | 1.0988          | 0.5991   |
| 1.1208        | 4.01  | 280  | 1.2279          | 0.5714   |
| 1.1588        | 5.01  | 336  | 0.8424          | 0.7097   |
| 1.0834        | 6.01  | 392  | 1.1035          | 0.5346   |
| 1.2194        | 7.01  | 448  | 1.0749          | 0.4839   |
| 0.8462        | 8.01  | 504  | 0.8755          | 0.6406   |
| 1.058         | 9.01  | 560  | 0.9025          | 0.6498   |
| 1.0163        | 10.01 | 616  | 1.2588          | 0.4839   |
| 1.0639        | 11.01 | 672  | 0.8928          | 0.6359   |
| 0.9317        | 12.01 | 728  | 0.8825          | 0.6221   |
| 0.9038        | 13.01 | 784  | 0.8765          | 0.5622   |
| 0.9155        | 14.01 | 840  | 0.8431          | 0.7005   |
| 1.0731        | 15.01 | 896  | 0.8175          | 0.7005   |
| 0.6864        | 16.01 | 952  | 1.0591          | 0.5853   |
| 0.9537        | 17.01 | 1008 | 0.9703          | 0.6221   |
| 0.7499        | 18.01 | 1064 | 0.8371          | 0.5806   |
| 0.7142        | 19.01 | 1120 | 0.9132          | 0.6636   |
| 0.675         | 20.01 | 1176 | 0.7597          | 0.6728   |
| 0.604         | 21.01 | 1232 | 1.2004          | 0.5714   |
| 0.7738        | 22.01 | 1288 | 1.0633          | 0.5668   |
| 0.7651        | 23.01 | 1344 | 0.6865          | 0.6820   |
| 0.6292        | 24.01 | 1400 | 0.7607          | 0.6912   |
| 0.7387        | 25.01 | 1456 | 1.3038          | 0.5346   |
| 0.7038        | 26.01 | 1512 | 1.2832          | 0.5530   |
| 0.7565        | 27.01 | 1568 | 0.8128          | 0.7005   |
| 0.6516        | 28.01 | 1624 | 1.0893          | 0.5392   |
| 0.7074        | 29.01 | 1680 | 1.0894          | 0.5991   |
| 0.4902        | 30.01 | 1736 | 1.0695          | 0.5622   |
| 0.4563        | 31.01 | 1792 | 1.2922          | 0.5300   |
| 0.7543        | 32.01 | 1848 | 0.8960          | 0.6820   |
| 0.7467        | 33.01 | 1904 | 0.7861          | 0.7465   |
| 0.6459        | 34.01 | 1960 | 1.2835          | 0.5622   |
| 0.7296        | 35.01 | 2016 | 1.0303          | 0.5806   |
| 0.5           | 36.01 | 2072 | 0.8924          | 0.6129   |
| 0.5181        | 37.01 | 2128 | 0.8769          | 0.7235   |
| 0.5225        | 38.01 | 2184 | 0.7288          | 0.7512   |
| 0.5617        | 39.01 | 2240 | 0.6330          | 0.7926   |
| 0.677         | 40.01 | 2296 | 0.7733          | 0.7419   |
| 0.6891        | 41.01 | 2352 | 0.7463          | 0.8157   |
| 0.6662        | 42.01 | 2408 | 0.9304          | 0.7235   |
| 0.4602        | 43.01 | 2464 | 1.5115          | 0.5207   |
| 0.581         | 44.01 | 2520 | 1.2296          | 0.6175   |
| 0.5418        | 45.01 | 2576 | 1.0070          | 0.6221   |
| 0.5199        | 46.01 | 2632 | 1.1344          | 0.6083   |
| 0.6876        | 47.01 | 2688 | 0.9800          | 0.5760   |
| 0.5165        | 48.01 | 2744 | 1.3709          | 0.5069   |
| 0.5727        | 49.01 | 2800 | 0.9960          | 0.6866   |
| 0.3698        | 50.01 | 2856 | 1.2246          | 0.5484   |
| 0.5836        | 51.01 | 2912 | 0.9892          | 0.6866   |
| 0.6017        | 52.01 | 2968 | 0.9388          | 0.6590   |
| 0.4851        | 53.01 | 3024 | 1.1415          | 0.6590   |
| 0.3038        | 54.01 | 3080 | 0.9413          | 0.6959   |
| 0.6075        | 55.01 | 3136 | 1.0467          | 0.6129   |
| 0.4474        | 56.01 | 3192 | 0.8436          | 0.6866   |
| 0.3711        | 57.01 | 3248 | 0.8994          | 0.6774   |
| 0.5279        | 58.01 | 3304 | 0.8859          | 0.7189   |
| 0.6032        | 59.01 | 3360 | 1.2931          | 0.6498   |
| 0.3282        | 60.01 | 3416 | 0.9435          | 0.7143   |
| 0.3506        | 61.01 | 3472 | 1.0971          | 0.6728   |
| 0.3169        | 62.01 | 3528 | 0.9101          | 0.7512   |
| 0.438         | 63.01 | 3584 | 1.4072          | 0.6359   |
| 0.5208        | 64.01 | 3640 | 1.2648          | 0.6544   |
| 0.4563        | 65.01 | 3696 | 1.1162          | 0.6498   |
| 0.6693        | 66.01 | 3752 | 1.8558          | 0.5576   |
| 0.5599        | 67.01 | 3808 | 1.6574          | 0.5392   |
| 0.4751        | 68.01 | 3864 | 1.1883          | 0.6129   |
| 0.6489        | 69.01 | 3920 | 1.2733          | 0.6129   |
| 0.4229        | 70.01 | 3976 | 1.0994          | 0.6682   |
| 0.4194        | 71.01 | 4032 | 1.1464          | 0.6175   |
| 0.2121        | 72.01 | 4088 | 1.1798          | 0.6175   |
| 0.4106        | 73.01 | 4144 | 1.3294          | 0.5806   |
| 0.3962        | 74.01 | 4200 | 1.4209          | 0.6359   |
| 0.2963        | 75.01 | 4256 | 1.5016          | 0.5945   |
| 0.5436        | 76.01 | 4312 | 1.5647          | 0.5484   |
| 0.4115        | 77.01 | 4368 | 1.4309          | 0.6037   |
| 0.1635        | 78.01 | 4424 | 1.3660          | 0.6452   |
| 0.2931        | 79.01 | 4480 | 1.3299          | 0.6498   |
| 0.5154        | 80.01 | 4536 | 1.6550          | 0.5806   |
| 0.2993        | 81.01 | 4592 | 1.6520          | 0.5991   |
| 0.4391        | 82.01 | 4648 | 1.3823          | 0.6406   |
| 0.485         | 83.01 | 4704 | 1.4860          | 0.6037   |
| 0.3313        | 84.01 | 4760 | 1.3875          | 0.6175   |
| 0.4194        | 85.01 | 4816 | 1.4334          | 0.5899   |
| 0.4515        | 86.01 | 4872 | 1.6489          | 0.5991   |
| 0.3283        | 87.01 | 4928 | 1.4549          | 0.6083   |
| 0.1914        | 88.01 | 4984 | 1.3415          | 0.6267   |
| 0.2142        | 89.01 | 5040 | 1.6426          | 0.6267   |
| 0.3121        | 90.01 | 5096 | 1.6999          | 0.6037   |
| 0.367         | 91.01 | 5152 | 1.4683          | 0.6083   |
| 0.178         | 92.01 | 5208 | 1.4665          | 0.6267   |
| 0.3972        | 93.01 | 5264 | 1.3464          | 0.6452   |
| 0.224         | 94.01 | 5320 | 1.5009          | 0.6175   |
| 0.1848        | 95.01 | 5376 | 1.5068          | 0.6129   |
| 0.2776        | 96.01 | 5432 | 1.5383          | 0.6175   |
| 0.3506        | 97.01 | 5488 | 1.5356          | 0.6129   |
| 0.401         | 98.01 | 5544 | 1.5504          | 0.6175   |
| 0.3466        | 99.0  | 5550 | 1.5505          | 0.6175   |


### Framework versions

- Transformers 4.36.2
- Pytorch 1.13.1
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-subset-100epochs", "results": []}]}
transformers safetensors videomae video-classification generated_from_trainer base_model:MCG-NJU/videomae-base license:cc-by-nc-4.0 endpoints_compatible region:us