---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-ft-92397
  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-92397

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: 1.1332
- Accuracy: 0.3386

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1076        | 0.0202  | 111  | 1.1240          | 0.3333   |
| 1.1466        | 1.0202  | 222  | 1.1166          | 0.3333   |
| 1.1022        | 2.0202  | 333  | 1.1221          | 0.3333   |
| 1.109         | 3.0202  | 444  | 1.1170          | 0.3333   |
| 1.1007        | 4.0202  | 555  | 1.1375          | 0.3333   |
| 1.1146        | 5.0202  | 666  | 1.1004          | 0.3386   |
| 1.1575        | 6.0202  | 777  | 1.1249          | 0.3333   |
| 1.0851        | 7.0202  | 888  | 1.1254          | 0.3439   |
| 1.1069        | 8.0202  | 999  | 1.1136          | 0.3333   |
| 1.0998        | 9.0202  | 1110 | 1.0945          | 0.3545   |
| 1.1289        | 10.0202 | 1221 | 1.0992          | 0.3439   |
| 1.0674        | 11.0202 | 1332 | 1.0957          | 0.3545   |
| 1.1144        | 12.0202 | 1443 | 1.1139          | 0.3228   |
| 1.0971        | 13.0202 | 1554 | 1.1089          | 0.3228   |
| 1.0704        | 14.0202 | 1665 | 1.1031          | 0.3333   |
| 1.1064        | 15.0202 | 1776 | 1.1003          | 0.3492   |
| 1.0782        | 16.0202 | 1887 | 1.1026          | 0.3386   |
| 1.1086        | 17.0202 | 1998 | 1.1091          | 0.3175   |
| 1.0911        | 18.0202 | 2109 | 1.0965          | 0.3386   |
| 1.0961        | 19.0202 | 2220 | 1.1108          | 0.3333   |
| 1.0967        | 20.0202 | 2331 | 1.1029          | 0.3175   |
| 1.0746        | 21.0202 | 2442 | 1.1127          | 0.3333   |
| 1.1076        | 22.0202 | 2553 | 1.0996          | 0.3492   |
| 1.0786        | 23.0202 | 2664 | 1.1138          | 0.3333   |
| 1.0819        | 24.0202 | 2775 | 1.0970          | 0.3651   |
| 1.1031        | 25.0202 | 2886 | 1.1135          | 0.3333   |
| 1.092         | 26.0202 | 2997 | 1.1050          | 0.3439   |
| 1.103         | 27.0202 | 3108 | 1.1039          | 0.3598   |
| 1.0903        | 28.0202 | 3219 | 1.1149          | 0.3333   |
| 1.1232        | 29.0202 | 3330 | 1.1062          | 0.3333   |
| 1.106         | 30.0202 | 3441 | 1.1124          | 0.3175   |
| 1.0607        | 31.0202 | 3552 | 1.1095          | 0.3333   |
| 1.0839        | 32.0202 | 3663 | 1.1083          | 0.3386   |
| 1.0867        | 33.0202 | 3774 | 1.1007          | 0.3545   |
| 1.0913        | 34.0202 | 3885 | 1.0996          | 0.3598   |
| 1.0567        | 35.0202 | 3996 | 1.0946          | 0.3386   |
| 1.0877        | 36.0202 | 4107 | 1.1004          | 0.3280   |
| 1.0828        | 37.0202 | 4218 | 1.1074          | 0.3228   |
| 1.131         | 38.0202 | 4329 | 1.0992          | 0.3122   |
| 1.0299        | 39.0202 | 4440 | 1.1035          | 0.3280   |
| 1.0864        | 40.0202 | 4551 | 1.0947          | 0.3386   |
| 1.0643        | 41.0202 | 4662 | 1.1006          | 0.3545   |
| 1.0687        | 42.0202 | 4773 | 1.1056          | 0.3280   |
| 1.0978        | 43.0202 | 4884 | 1.0907          | 0.3598   |
| 1.0273        | 44.0202 | 4995 | 1.0969          | 0.3439   |
| 1.0459        | 45.0202 | 5106 | 1.1021          | 0.3492   |
| 1.0561        | 46.0202 | 5217 | 1.1003          | 0.3386   |
| 1.0482        | 47.0202 | 5328 | 1.1028          | 0.3386   |
| 1.0916        | 48.0202 | 5439 | 1.1053          | 0.3545   |
| 1.0729        | 49.0111 | 5500 | 1.1055          | 0.3545   |


### Framework versions

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