---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-ucf101-subset
  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-finetuned-ucf101-subset

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: 0.6545
- Accuracy: 0.8339

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 7060

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1788        | 0.0494  | 349  | 1.1337          | 0.5608   |
| 0.93          | 1.0494  | 698  | 1.0407          | 0.6386   |
| 0.7623        | 2.0494  | 1047 | 0.8954          | 0.6775   |
| 0.6178        | 3.0494  | 1396 | 0.8011          | 0.7050   |
| 0.4934        | 4.0494  | 1745 | 0.8328          | 0.7034   |
| 0.5447        | 5.0494  | 2094 | 0.7323          | 0.7731   |
| 0.4106        | 6.0494  | 2443 | 0.6595          | 0.7763   |
| 0.4948        | 7.0494  | 2792 | 0.6986          | 0.7780   |
| 0.3257        | 8.0494  | 3141 | 0.5867          | 0.8152   |
| 0.2109        | 9.0494  | 3490 | 0.6624          | 0.8039   |
| 0.3482        | 10.0494 | 3839 | 0.6958          | 0.8104   |
| 0.4104        | 11.0494 | 4188 | 0.5619          | 0.8331   |
| 0.2176        | 12.0494 | 4537 | 0.5727          | 0.8217   |
| 0.1634        | 13.0494 | 4886 | 0.4984          | 0.8606   |
| 0.1256        | 14.0494 | 5235 | 0.5515          | 0.8444   |
| 0.188         | 15.0494 | 5584 | 0.5470          | 0.8428   |
| 0.2174        | 16.0494 | 5933 | 0.4847          | 0.8590   |
| 0.1619        | 17.0494 | 6282 | 0.5030          | 0.8784   |
| 0.3518        | 18.0494 | 6631 | 0.4783          | 0.8784   |
| 0.1533        | 19.0494 | 6980 | 0.5133          | 0.8703   |
| 0.101         | 20.0113 | 7060 | 0.5090          | 0.8703   |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.0.1+cu117
- Datasets 2.21.0
- Tokenizers 0.19.1