---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-0505-original
  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-0505-original

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: 0.7704
- Accuracy: 0.74

## 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: 520

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.4346        | 0.1019 | 53   | 2.1892          | 0.18     |
| 1.2302        | 1.1019 | 106  | 1.6488          | 0.44     |
| 0.7052        | 2.1019 | 159  | 1.1180          | 0.66     |
| 0.1088        | 3.1019 | 212  | 0.7580          | 0.74     |
| 0.0163        | 4.1019 | 265  | 0.9034          | 0.72     |
| 0.0039        | 5.1019 | 318  | 0.8373          | 0.8      |
| 0.0024        | 6.1019 | 371  | 0.7717          | 0.76     |
| 0.0019        | 7.1019 | 424  | 0.7680          | 0.74     |
| 0.0016        | 8.1019 | 477  | 0.7709          | 0.74     |
| 0.0017        | 9.0827 | 520  | 0.7704          | 0.74     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1