---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cont-vvt-gs-rot-flip-wtoken-f198-4.4-h768-t8.16.16

This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6569
- Accuracy: 0.7407

## 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: 2e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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: 5500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.868         | 0.0402  | 221  | 0.7216          | 0.6931   |
| 0.715         | 1.0402  | 442  | 0.6954          | 0.7037   |
| 0.7041        | 2.0402  | 663  | 0.7269          | 0.7460   |
| 0.5228        | 3.0402  | 884  | 0.7158          | 0.7407   |
| 0.7336        | 4.0402  | 1105 | 0.6833          | 0.7196   |
| 0.5987        | 5.0402  | 1326 | 0.6155          | 0.7725   |
| 0.8574        | 6.0402  | 1547 | 0.6601          | 0.7302   |
| 0.6805        | 7.0402  | 1768 | 0.6374          | 0.7460   |
| 0.8086        | 8.0402  | 1989 | 0.6896          | 0.6984   |
| 0.6552        | 9.0402  | 2210 | 0.6535          | 0.7090   |
| 0.7846        | 10.0402 | 2431 | 0.6646          | 0.7354   |
| 0.6114        | 11.0402 | 2652 | 0.6111          | 0.7619   |
| 0.7435        | 12.0402 | 2873 | 0.6779          | 0.7354   |
| 0.7742        | 13.0402 | 3094 | 0.7390          | 0.6878   |
| 0.6558        | 14.0402 | 3315 | 0.6284          | 0.7354   |
| 0.4822        | 15.0402 | 3536 | 0.7071          | 0.7196   |
| 0.7686        | 16.0402 | 3757 | 0.6982          | 0.7302   |
| 0.7945        | 17.0402 | 3978 | 0.6336          | 0.7566   |
| 0.5755        | 18.0402 | 4199 | 0.5924          | 0.7460   |
| 0.6895        | 19.0402 | 4420 | 0.6227          | 0.7513   |
| 0.4775        | 20.0402 | 4641 | 0.5846          | 0.7672   |
| 0.8137        | 21.0402 | 4862 | 0.6724          | 0.7354   |
| 0.4226        | 22.0402 | 5083 | 0.6772          | 0.7460   |
| 0.6616        | 23.0402 | 5304 | 0.6856          | 0.7407   |
| 0.5246        | 24.0356 | 5500 | 0.6569          | 0.7407   |


### Framework versions

- Transformers 4.41.2
- Pytorch 1.13.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1