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---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
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-finetuned-ucf101-subset
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: 0.7149
- Accuracy: 0.9038
## 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: 1
- eval_batch_size: 1
- 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: 15000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0174 | 0.02 | 300 | 0.2750 | 0.9423 |
| 0.0218 | 1.02 | 600 | 0.7020 | 0.8269 |
| 0.5348 | 2.02 | 900 | 1.1836 | 0.7692 |
| 0.9667 | 3.02 | 1200 | 1.9316 | 0.5962 |
| 2.0242 | 4.02 | 1500 | 1.5680 | 0.6923 |
| 0.7852 | 5.02 | 1800 | 0.5868 | 0.9038 |
| 1.9104 | 6.02 | 2100 | 1.8121 | 0.7115 |
| 1.1466 | 7.02 | 2400 | 1.3801 | 0.75 |
| 0.0025 | 8.02 | 2700 | 1.2799 | 0.7692 |
| 0.0005 | 9.02 | 3000 | 1.8073 | 0.7115 |
| 0.0005 | 10.02 | 3300 | 0.4820 | 0.9231 |
| 0.5816 | 11.02 | 3600 | 0.7625 | 0.8846 |
| 0.0014 | 12.02 | 3900 | 0.4762 | 0.9231 |
| 0.0694 | 13.02 | 4200 | 1.3250 | 0.8077 |
| 0.0002 | 14.02 | 4500 | 0.9637 | 0.8654 |
| 0.0003 | 15.02 | 4800 | 0.4808 | 0.9231 |
| 0.0003 | 16.02 | 5100 | 0.8623 | 0.8846 |
| 0.0002 | 17.02 | 5400 | 0.6881 | 0.9231 |
| 0.0004 | 18.02 | 5700 | 0.5577 | 0.9038 |
| 0.0001 | 19.02 | 6000 | 0.5069 | 0.9231 |
| 0.4994 | 20.02 | 6300 | 0.3667 | 0.9423 |
| 0.0002 | 21.02 | 6600 | 0.3666 | 0.9423 |
| 1.0279 | 22.02 | 6900 | 1.0781 | 0.8654 |
| 0.0135 | 23.02 | 7200 | 2.2670 | 0.7308 |
| 0.0002 | 24.02 | 7500 | 0.1732 | 0.9615 |
| 0.0002 | 25.02 | 7800 | 0.4422 | 0.9423 |
| 0.0001 | 26.02 | 8100 | 0.8196 | 0.8846 |
| 0.0001 | 27.02 | 8400 | 0.8037 | 0.8846 |
| 0.0001 | 28.02 | 8700 | 0.8696 | 0.8846 |
| 0.0002 | 29.02 | 9000 | 0.7887 | 0.9231 |
| 0.7745 | 30.02 | 9300 | 0.3868 | 0.9423 |
| 0.0001 | 31.02 | 9600 | 0.4386 | 0.9423 |
| 0.0002 | 32.02 | 9900 | 0.4036 | 0.9423 |
| 0.0001 | 33.02 | 10200 | 0.3513 | 0.9423 |
| 0.0001 | 34.02 | 10500 | 0.3075 | 0.9423 |
| 0.0001 | 35.02 | 10800 | 0.5712 | 0.9231 |
| 0.0005 | 36.02 | 11100 | 0.6482 | 0.9231 |
| 0.0001 | 37.02 | 11400 | 0.8843 | 0.9038 |
| 0.0001 | 38.02 | 11700 | 0.9147 | 0.8846 |
| 0.0001 | 39.02 | 12000 | 0.6891 | 0.9038 |
| 0.0001 | 40.02 | 12300 | 0.8976 | 0.8846 |
| 0.0001 | 41.02 | 12600 | 1.6405 | 0.8077 |
| 0.0001 | 42.02 | 12900 | 1.0550 | 0.8654 |
| 0.0 | 43.02 | 13200 | 1.0356 | 0.8654 |
| 0.0 | 44.02 | 13500 | 1.0037 | 0.8462 |
| 0.0 | 45.02 | 13800 | 0.9632 | 0.8654 |
| 0.0 | 46.02 | 14100 | 0.6649 | 0.9231 |
| 0.0 | 47.02 | 14400 | 0.8702 | 0.8846 |
| 0.0 | 48.02 | 14700 | 0.7201 | 0.9038 |
| 0.0 | 49.02 | 15000 | 0.7149 | 0.9038 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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