---
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_31
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vivit-b-16x2-kinetics400-finetuned-cricket_shot_detection_31

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: 1.0141
- Accuracy: 0.6667
- F1: 0.6537
- Recall: 0.6667
- Precision: 0.75

## 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: 4e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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: 0.1
- training_steps: 4624

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.7482        | 0.0314 | 145  | 1.7665          | 0.2      | 0.1133 | 0.2    | 0.0794    |
| 1.3969        | 1.0314 | 290  | 1.5064          | 0.6      | 0.5700 | 0.6    | 0.6422    |
| 1.0075        | 2.0314 | 435  | 1.2230          | 0.5333   | 0.5467 | 0.5333 | 0.5778    |
| 0.805         | 3.0314 | 580  | 0.9838          | 0.6667   | 0.6537 | 0.6667 | 0.75      |
| 0.9045        | 4.0314 | 725  | 1.0228          | 0.6      | 0.5676 | 0.6    | 0.5444    |
| 0.4916        | 5.0314 | 870  | 1.0251          | 0.6667   | 0.6448 | 0.6667 | 0.7056    |
| 0.2283        | 6.0314 | 1015 | 1.0003          | 0.6667   | 0.6537 | 0.6667 | 0.75      |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0