---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-ft-48192
  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. -->

# vivit-b-16x2-kinetics400-ft-48192

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: 0.7001
- Accuracy: 0.7302

## 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: 5500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0936        | 0.0202  | 111  | 1.1342          | 0.3280   |
| 1.0023        | 1.0202  | 222  | 0.9602          | 0.5291   |
| 1.1132        | 2.0202  | 333  | 1.0361          | 0.4603   |
| 0.9459        | 3.0202  | 444  | 1.0181          | 0.4603   |
| 0.9839        | 4.0202  | 555  | 0.9418          | 0.5767   |
| 1.0077        | 5.0202  | 666  | 0.9735          | 0.5344   |
| 1.046         | 6.0202  | 777  | 0.8630          | 0.6032   |
| 1.2237        | 7.0202  | 888  | 0.8513          | 0.6455   |
| 0.8309        | 8.0202  | 999  | 1.0534          | 0.5344   |
| 0.9748        | 9.0202  | 1110 | 0.8717          | 0.6402   |
| 0.9053        | 10.0202 | 1221 | 0.9555          | 0.5291   |
| 0.8577        | 11.0202 | 1332 | 0.9219          | 0.5820   |
| 0.9806        | 12.0202 | 1443 | 0.9808          | 0.5397   |
| 0.7957        | 13.0202 | 1554 | 0.9395          | 0.5820   |
| 0.787         | 14.0202 | 1665 | 0.8645          | 0.5979   |
| 0.8045        | 15.0202 | 1776 | 0.9837          | 0.5450   |
| 0.7793        | 16.0202 | 1887 | 0.9503          | 0.5767   |
| 0.68          | 17.0202 | 1998 | 0.8213          | 0.6667   |
| 0.8641        | 18.0202 | 2109 | 0.8046          | 0.6772   |
| 0.6624        | 19.0202 | 2220 | 0.7309          | 0.6984   |
| 0.6083        | 20.0202 | 2331 | 0.8542          | 0.6508   |
| 0.6252        | 21.0202 | 2442 | 0.7641          | 0.6878   |
| 0.6654        | 22.0202 | 2553 | 0.8472          | 0.6931   |
| 0.5135        | 23.0202 | 2664 | 0.7514          | 0.6614   |
| 0.6607        | 24.0202 | 2775 | 0.9482          | 0.5979   |
| 0.6489        | 25.0202 | 2886 | 1.7302          | 0.4497   |
| 0.5558        | 26.0202 | 2997 | 1.1263          | 0.5661   |
| 0.7327        | 27.0202 | 3108 | 0.7243          | 0.6878   |
| 0.5886        | 28.0202 | 3219 | 0.6610          | 0.7143   |
| 0.7143        | 29.0202 | 3330 | 0.7716          | 0.6667   |
| 0.5154        | 30.0202 | 3441 | 0.9681          | 0.6138   |
| 0.5505        | 31.0202 | 3552 | 0.9641          | 0.6349   |
| 0.6           | 32.0202 | 3663 | 0.7182          | 0.6984   |
| 0.6814        | 33.0202 | 3774 | 0.7108          | 0.7090   |
| 0.7797        | 34.0202 | 3885 | 0.7822          | 0.6984   |
| 0.5997        | 35.0202 | 3996 | 0.7895          | 0.6984   |
| 0.6454        | 36.0202 | 4107 | 0.8025          | 0.7090   |
| 0.5421        | 37.0202 | 4218 | 0.7742          | 0.6825   |
| 0.5026        | 38.0202 | 4329 | 0.6991          | 0.7302   |
| 0.5058        | 39.0202 | 4440 | 0.7855          | 0.6667   |
| 0.5395        | 40.0202 | 4551 | 0.7164          | 0.7196   |
| 0.5196        | 41.0202 | 4662 | 0.8336          | 0.6931   |
| 0.3783        | 42.0202 | 4773 | 0.7769          | 0.7143   |
| 0.5183        | 43.0202 | 4884 | 0.8074          | 0.6984   |
| 0.4013        | 44.0202 | 4995 | 0.7954          | 0.7196   |
| 0.5218        | 45.0202 | 5106 | 0.7939          | 0.7090   |
| 0.52          | 46.0202 | 5217 | 0.7603          | 0.7143   |
| 0.409         | 47.0202 | 5328 | 0.8217          | 0.7143   |
| 0.4437        | 48.0202 | 5439 | 0.8168          | 0.7143   |
| 0.415         | 49.0111 | 5500 | 0.8344          | 0.7196   |


### Framework versions

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