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

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: 1.9485
- Accuracy: 0.2427

## 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: 2100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4781        | 0.09  | 196  | 1.8166          | 0.2439   |
| 2.0142        | 1.09  | 392  | 2.2946          | 0.1951   |
| 1.2947        | 2.09  | 588  | 1.6998          | 0.3659   |
| 0.8486        | 3.09  | 784  | 2.0369          | 0.2195   |
| 0.2636        | 4.09  | 980  | 1.9748          | 0.3171   |
| 0.2805        | 5.09  | 1176 | 2.3563          | 0.3659   |
| 0.0923        | 6.09  | 1372 | 2.3754          | 0.3659   |
| 0.1543        | 7.09  | 1568 | 2.7737          | 0.3171   |
| 0.0387        | 8.09  | 1764 | 2.6676          | 0.3659   |
| 0.0101        | 9.09  | 1960 | 2.7895          | 0.3415   |
| 0.0662        | 10.07 | 2100 | 2.7728          | 0.3415   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2