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

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: 1.8988
- Accuracy: 0.6953

## 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: 1
- eval_batch_size: 1
- 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: 3430

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8386        | 0.1   | 343  | 1.5448          | 0.5448   |
| 1.1419        | 1.1   | 686  | 1.2918          | 0.5412   |
| 0.778         | 2.1   | 1029 | 1.4229          | 0.7240   |
| 0.7591        | 3.1   | 1372 | 1.5418          | 0.6918   |
| 0.8103        | 4.1   | 1715 | 1.3608          | 0.6810   |
| 0.3701        | 5.1   | 2058 | 1.6575          | 0.6810   |
| 0.2027        | 6.1   | 2401 | 1.8233          | 0.6774   |
| 0.0002        | 7.1   | 2744 | 1.9324          | 0.6738   |
| 0.1793        | 8.1   | 3087 | 1.8483          | 0.6953   |
| 0.0007        | 9.1   | 3430 | 1.8988          | 0.6953   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.0.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1