---
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-without-pretrained
  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-without-pretrained

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: 2.1644
- Accuracy: 0.4138

## 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2964        | 0.1   | 300  | 2.4749          | 0.1081   |
| 3.1346        | 1.1   | 600  | 2.4779          | 0.1081   |
| 1.9796        | 2.1   | 900  | 2.4339          | 0.2973   |
| 1.8692        | 3.1   | 1200 | 2.1774          | 0.3243   |
| 1.3617        | 4.1   | 1500 | 2.1613          | 0.3243   |
| 2.0821        | 5.1   | 1800 | 1.9207          | 0.2162   |
| 1.5534        | 6.1   | 2100 | 2.2003          | 0.3514   |
| 0.5632        | 7.1   | 2400 | 3.0211          | 0.3514   |
| 0.2806        | 8.1   | 2700 | 2.7735          | 0.3514   |
| 1.3832        | 9.1   | 3000 | 2.9315          | 0.3514   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2