---
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-cricket_shot_detection_14
  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-cricket_shot_detection_14

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: 1.0573
- Accuracy: 0.6316
- F1: 0.6559
- Recall: 0.6316
- Precision: 0.7018

## 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: 7e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- training_steps: 576

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.6885        | 0.1267 | 73   | 1.7082          | 0.1579   | 0.1853 | 0.1579 | 0.4684    |
| 1.2348        | 1.1267 | 146  | 1.3958          | 0.4211   | 0.4354 | 0.4211 | 0.5088    |
| 0.8271        | 2.1267 | 219  | 1.1646          | 0.4737   | 0.5416 | 0.4737 | 0.6754    |
| 0.688         | 3.1267 | 292  | 1.0573          | 0.6316   | 0.6559 | 0.6316 | 0.7018    |
| 0.6263        | 4.1267 | 365  | 1.0327          | 0.5789   | 0.5982 | 0.5789 | 0.6579    |
| 0.4942        | 5.1267 | 438  | 1.0107          | 0.6316   | 0.6333 | 0.6316 | 0.6535    |
| 0.3217        | 6.1267 | 511  | 1.0271          | 0.5789   | 0.5982 | 0.5789 | 0.6579    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0