---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-ft-3620
  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-3620

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: 0.9281
- Accuracy: 0.5566

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.0684        | 0.0202  | 111  | 1.1114          | 0.3799   |
| 1.0415        | 1.0202  | 222  | 1.0135          | 0.5249   |
| 1.0271        | 2.0202  | 333  | 1.0630          | 0.4857   |
| 1.1609        | 3.0202  | 444  | 1.0203          | 0.4222   |
| 0.9824        | 4.0202  | 555  | 1.0219          | 0.5249   |
| 1.0247        | 5.0202  | 666  | 1.0210          | 0.5026   |
| 1.0824        | 6.0202  | 777  | 0.9947          | 0.4720   |
| 0.943         | 7.0202  | 888  | 1.1085          | 0.4381   |
| 0.8807        | 8.0202  | 999  | 0.9345          | 0.5767   |
| 1.1009        | 9.0202  | 1110 | 0.9855          | 0.5164   |
| 1.0292        | 10.0202 | 1221 | 1.0506          | 0.4339   |
| 0.9071        | 11.0202 | 1332 | 0.9926          | 0.5143   |
| 1.0001        | 12.0202 | 1443 | 1.0406          | 0.4931   |
| 0.9698        | 13.0202 | 1554 | 0.9440          | 0.5598   |
| 0.9405        | 14.0202 | 1665 | 0.9667          | 0.5323   |
| 0.8802        | 15.0202 | 1776 | 0.9011          | 0.5862   |
| 0.9154        | 16.0202 | 1887 | 0.9429          | 0.5598   |
| 0.929         | 17.0202 | 1998 | 0.9948          | 0.5132   |
| 0.9112        | 18.0202 | 2109 | 0.9056          | 0.5852   |
| 0.9202        | 19.0202 | 2220 | 0.9489          | 0.5524   |
| 0.9004        | 20.0202 | 2331 | 0.8995          | 0.5820   |
| 0.9318        | 21.0202 | 2442 | 0.9032          | 0.5958   |
| 0.8493        | 22.0202 | 2553 | 0.9975          | 0.5238   |
| 0.8587        | 23.0202 | 2664 | 1.0142          | 0.5259   |
| 0.958         | 24.0202 | 2775 | 0.9665          | 0.5376   |
| 0.996         | 25.0202 | 2886 | 0.9391          | 0.5704   |
| 0.823         | 26.0202 | 2997 | 0.9171          | 0.5778   |
| 0.8834        | 27.0202 | 3108 | 0.8923          | 0.5873   |
| 0.8615        | 28.0202 | 3219 | 0.9577          | 0.5471   |
| 0.9462        | 29.0202 | 3330 | 0.9468          | 0.5630   |
| 0.8909        | 30.0202 | 3441 | 0.9343          | 0.5672   |
| 0.8048        | 31.0202 | 3552 | 0.9107          | 0.5778   |
| 0.8109        | 32.0202 | 3663 | 0.9547          | 0.5492   |
| 0.9242        | 33.0202 | 3774 | 0.9275          | 0.5598   |
| 0.9046        | 34.0202 | 3885 | 0.9290          | 0.5831   |
| 0.7677        | 35.0202 | 3996 | 0.9208          | 0.5725   |
| 0.8501        | 36.0202 | 4107 | 0.9126          | 0.5810   |
| 0.8468        | 37.0202 | 4218 | 0.9053          | 0.5862   |
| 0.7814        | 38.0202 | 4329 | 0.8858          | 0.5905   |
| 0.9354        | 39.0202 | 4440 | 0.9207          | 0.5725   |
| 0.8849        | 40.0202 | 4551 | 0.9277          | 0.5651   |
| 0.7856        | 41.0202 | 4662 | 0.9130          | 0.5915   |
| 0.7133        | 42.0202 | 4773 | 0.9080          | 0.5884   |
| 0.932         | 43.0202 | 4884 | 0.9388          | 0.5577   |
| 0.6883        | 44.0202 | 4995 | 0.8925          | 0.5937   |
| 0.9944        | 45.0202 | 5106 | 0.9143          | 0.5820   |
| 0.8892        | 46.0202 | 5217 | 0.9103          | 0.5884   |
| 0.9071        | 47.0202 | 5328 | 0.9018          | 0.5905   |
| 0.7943        | 48.0202 | 5439 | 0.9022          | 0.5905   |
| 0.8034        | 49.0111 | 5500 | 0.9004          | 0.5947   |


### Framework versions

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