---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-0508-checking-original_mediapipe
  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-kinetics400-0508-checking-original_mediapipe

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: 0.8456
- Accuracy: 0.72

## 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: 2
- eval_batch_size: 2
- 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3395        | 0.1   | 50   | 2.1400          | 0.16     |
| 1.4353        | 1.1   | 100  | 1.4633          | 0.56     |
| 0.3947        | 2.1   | 150  | 0.9567          | 0.68     |
| 0.1572        | 3.1   | 200  | 0.8107          | 0.8      |
| 0.0156        | 4.1   | 250  | 0.9614          | 0.62     |
| 0.0047        | 5.1   | 300  | 0.8670          | 0.72     |
| 0.0029        | 6.1   | 350  | 0.8325          | 0.76     |
| 0.0024        | 7.1   | 400  | 0.8589          | 0.74     |
| 0.002         | 8.1   | 450  | 0.8479          | 0.74     |
| 0.0017        | 9.1   | 500  | 0.8456          | 0.72     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1