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---
library_name: transformers
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: 0.3320
- Accuracy: 0.9290
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 600
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.3375 | 0.0617 | 37 | 2.1297 | 0.3 |
| 1.7263 | 1.0625 | 75 | 1.4907 | 0.4714 |
| 0.675 | 2.0617 | 112 | 0.6070 | 0.8714 |
| 0.3702 | 3.0625 | 150 | 0.4089 | 0.8571 |
| 0.205 | 4.0617 | 187 | 0.4285 | 0.8286 |
| 0.3209 | 5.0625 | 225 | 0.2749 | 0.8714 |
| 0.1253 | 6.0617 | 262 | 0.0571 | 0.9857 |
| 0.1052 | 7.0625 | 300 | 0.2550 | 0.9429 |
| 0.1586 | 8.0617 | 337 | 0.1588 | 0.9429 |
| 0.0498 | 9.0625 | 375 | 0.0736 | 0.9857 |
| 0.0958 | 10.0617 | 412 | 0.0911 | 0.9571 |
| 0.1095 | 11.0625 | 450 | 0.0448 | 0.9857 |
| 0.0065 | 12.0617 | 487 | 0.0734 | 0.9857 |
| 0.0019 | 13.0625 | 525 | 0.0603 | 0.9857 |
| 0.0018 | 14.0617 | 562 | 0.0607 | 0.9857 |
| 0.0018 | 15.0625 | 600 | 0.0634 | 0.9857 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.0.1+cu117
- Datasets 3.2.0
- Tokenizers 0.20.3
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