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---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vivit-surf-analytics-runpod
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-surf-analytics-runpod
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: 0.7609
- Accuracy: 0.9163
- F1: 0.9154
## 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: 22230
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| 0.0 | 0.0333 | 741 | 0.9082 | 0.9070 | 0.9068 |
| 0.5976 | 1.0333 | 1482 | 2.5471 | 0.7302 | 0.7286 |
| 0.1188 | 2.0333 | 2223 | 1.0145 | 0.8698 | 0.8695 |
| 0.0001 | 3.0333 | 2964 | 1.1956 | 0.8465 | 0.8384 |
| 0.5026 | 4.0333 | 3705 | 0.8190 | 0.8651 | 0.8608 |
| 0.0 | 5.0333 | 4446 | 1.1466 | 0.8372 | 0.8377 |
| 0.0 | 6.0333 | 5187 | 1.0804 | 0.8419 | 0.8358 |
| 0.0 | 7.0333 | 5928 | 0.8535 | 0.8930 | 0.8909 |
| 0.0 | 8.0333 | 6669 | 0.6512 | 0.9070 | 0.9070 |
| 0.0001 | 9.0333 | 7410 | 0.8475 | 0.8884 | 0.8887 |
| 0.0001 | 10.0333 | 8151 | 0.7335 | 0.8977 | 0.8972 |
| 0.0 | 11.0333 | 8892 | 0.7774 | 0.9070 | 0.9054 |
| 0.0 | 12.0333 | 9633 | 0.7346 | 0.9116 | 0.9107 |
| 0.0 | 13.0333 | 10374 | 0.7609 | 0.9163 | 0.9154 |
| 0.0 | 14.0333 | 11115 | 0.7560 | 0.9070 | 0.9074 |
| 0.0 | 15.0333 | 11856 | 0.8036 | 0.9163 | 0.9151 |
| 0.0 | 16.0333 | 12597 | 0.7962 | 0.9163 | 0.9151 |
| 0.0 | 17.0333 | 13338 | 0.7821 | 0.9163 | 0.9147 |
| 0.0 | 18.0333 | 14079 | 0.7898 | 0.9163 | 0.9149 |
| 0.0 | 19.0333 | 14820 | 1.0166 | 0.8791 | 0.8748 |
| 0.0 | 20.0333 | 15561 | 0.8697 | 0.8977 | 0.8968 |
| 0.0 | 21.0333 | 16302 | 0.7670 | 0.9023 | 0.9017 |
| 0.0 | 22.0333 | 17043 | 0.7399 | 0.9116 | 0.9107 |
| 0.0 | 23.0333 | 17784 | 0.7458 | 0.9116 | 0.9107 |
| 0.0 | 24.0333 | 18525 | 0.7701 | 0.8977 | 0.8969 |
| 0.0 | 25.0333 | 19266 | 0.7924 | 0.9023 | 0.9014 |
| 0.0 | 26.0333 | 20007 | 0.7955 | 0.9023 | 0.9014 |
| 0.0 | 27.0333 | 20748 | 0.8675 | 0.8977 | 0.8969 |
| 0.0 | 28.0333 | 21489 | 0.8671 | 0.8977 | 0.8969 |
| 0.0 | 29.0333 | 22230 | 0.8665 | 0.8977 | 0.8969 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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