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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-subset-0401
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-subset-0401
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: 0.6379
- Accuracy: 0.7824
## 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: 2775
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6048 | 0.02 | 56 | 1.6213 | 0.0829 |
| 1.5891 | 1.02 | 112 | 1.5230 | 0.2811 |
| 1.4797 | 2.02 | 168 | 1.6437 | 0.1982 |
| 1.3999 | 3.02 | 224 | 0.9263 | 0.7465 |
| 1.0917 | 4.02 | 280 | 1.2308 | 0.4931 |
| 1.238 | 5.02 | 336 | 0.9406 | 0.6590 |
| 1.1525 | 6.02 | 392 | 0.8809 | 0.7051 |
| 1.0806 | 7.02 | 448 | 1.0089 | 0.5945 |
| 0.8483 | 8.02 | 504 | 0.9700 | 0.5853 |
| 0.992 | 9.02 | 560 | 1.1880 | 0.4885 |
| 0.862 | 10.02 | 616 | 0.7174 | 0.7512 |
| 1.0694 | 11.02 | 672 | 0.8598 | 0.7143 |
| 0.8885 | 12.02 | 728 | 0.8290 | 0.7097 |
| 0.8965 | 13.02 | 784 | 0.8304 | 0.7143 |
| 0.7371 | 14.02 | 840 | 0.7009 | 0.7696 |
| 0.6872 | 15.02 | 896 | 0.6768 | 0.7926 |
| 0.6022 | 16.02 | 952 | 0.7513 | 0.7373 |
| 0.9308 | 17.02 | 1008 | 0.8055 | 0.7097 |
| 0.4456 | 18.02 | 1064 | 0.7876 | 0.6728 |
| 0.6802 | 19.02 | 1120 | 0.7224 | 0.7235 |
| 0.7154 | 20.02 | 1176 | 0.7434 | 0.7051 |
| 0.503 | 21.02 | 1232 | 0.8346 | 0.6959 |
| 0.7203 | 22.02 | 1288 | 0.9694 | 0.5991 |
| 0.6799 | 23.02 | 1344 | 0.6474 | 0.7696 |
| 0.5802 | 24.02 | 1400 | 0.9573 | 0.6359 |
| 0.7047 | 25.02 | 1456 | 0.9120 | 0.6959 |
| 0.6701 | 26.02 | 1512 | 1.1690 | 0.5853 |
| 0.5514 | 27.02 | 1568 | 0.9174 | 0.6866 |
| 0.538 | 28.02 | 1624 | 0.8543 | 0.6866 |
| 0.7226 | 29.02 | 1680 | 0.7774 | 0.7465 |
| 0.4459 | 30.02 | 1736 | 0.9135 | 0.6359 |
| 0.3905 | 31.02 | 1792 | 0.8586 | 0.6728 |
| 0.7071 | 32.02 | 1848 | 0.7919 | 0.7327 |
| 0.4983 | 33.02 | 1904 | 0.7507 | 0.7512 |
| 0.5654 | 34.02 | 1960 | 0.7679 | 0.7143 |
| 0.5569 | 35.02 | 2016 | 0.8438 | 0.7097 |
| 0.3998 | 36.02 | 2072 | 0.8691 | 0.7189 |
| 0.5341 | 37.02 | 2128 | 0.8056 | 0.7604 |
| 0.4024 | 38.02 | 2184 | 0.7071 | 0.7880 |
| 0.5011 | 39.02 | 2240 | 0.8827 | 0.7005 |
| 0.5857 | 40.02 | 2296 | 0.8525 | 0.7097 |
| 0.5619 | 41.02 | 2352 | 0.8228 | 0.7512 |
| 0.6052 | 42.02 | 2408 | 0.8320 | 0.7373 |
| 0.5124 | 43.02 | 2464 | 0.8776 | 0.7419 |
| 0.3323 | 44.02 | 2520 | 0.8515 | 0.7465 |
| 0.5684 | 45.02 | 2576 | 0.9309 | 0.7097 |
| 0.4406 | 46.02 | 2632 | 0.8826 | 0.7465 |
| 0.6164 | 47.02 | 2688 | 0.8994 | 0.6959 |
| 0.4549 | 48.02 | 2744 | 0.8700 | 0.7189 |
| 0.3453 | 49.01 | 2775 | 0.8822 | 0.7189 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1
- Datasets 2.16.1
- Tokenizers 0.15.0
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