---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-allkisa-crop-background-0228-clip_duration
  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-kinetics-allkisa-crop-background-0228-clip_duration

This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3168
- Accuracy: 0.9393

## 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: 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: 0.05
- training_steps: 26400

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.0014        | 0.0100  | 265  | 0.5277          | 0.7871   |
| 0.1062        | 1.0100  | 530  | 0.4787          | 0.8080   |
| 0.0267        | 2.0100  | 795  | 0.5039          | 0.8429   |
| 0.0023        | 3.0100  | 1060 | 1.0419          | 0.7033   |
| 0.0003        | 4.0100  | 1325 | 0.5443          | 0.8115   |
| 0.0002        | 5.0100  | 1590 | 1.1036          | 0.7644   |
| 0.0006        | 6.0100  | 1855 | 0.4425          | 0.8551   |
| 3.5553        | 7.0100  | 2120 | 0.4898          | 0.8586   |
| 0.0002        | 8.0100  | 2385 | 0.5075          | 0.8482   |
| 0.0           | 9.0100  | 2650 | 0.5837          | 0.8674   |
| 0.0001        | 10.0100 | 2915 | 0.9025          | 0.8255   |
| 0.0001        | 11.0100 | 3180 | 0.6071          | 0.8691   |
| 0.0           | 12.0100 | 3445 | 0.6740          | 0.8639   |
| 0.0           | 13.0100 | 3710 | 0.9123          | 0.8272   |
| 0.0           | 14.0100 | 3975 | 0.7934          | 0.8517   |
| 0.0           | 15.0100 | 4240 | 0.6899          | 0.8674   |
| 0.0           | 16.0100 | 4505 | 0.7532          | 0.8709   |
| 0.0           | 17.0100 | 4770 | 0.7553          | 0.8691   |
| 0.0002        | 18.0100 | 5035 | 0.7433          | 0.8691   |
| 0.0           | 19.0100 | 5300 | 0.6715          | 0.8813   |
| 0.0           | 20.0100 | 5565 | 0.7857          | 0.8656   |
| 0.0           | 21.0100 | 5830 | 0.6759          | 0.8918   |
| 0.0           | 22.0100 | 6095 | 0.6435          | 0.8901   |
| 0.0           | 23.0100 | 6360 | 0.6853          | 0.8866   |
| 0.0           | 24.0100 | 6625 | 0.6530          | 0.8831   |
| 0.0038        | 25.0100 | 6890 | 0.7277          | 0.8743   |
| 0.0001        | 26.0100 | 7155 | 0.7137          | 0.8778   |
| 0.0           | 27.0100 | 7420 | 0.7081          | 0.8796   |
| 0.0           | 28.0100 | 7685 | 0.6977          | 0.8778   |
| 0.0           | 29.0100 | 7950 | 0.7008          | 0.8831   |
| 0.0004        | 30.0100 | 8215 | 0.7023          | 0.8813   |
| 0.0           | 31.0100 | 8480 | 0.7248          | 0.8796   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0