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README.md
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---
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library_name: transformers
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license: mit
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base_model: google/vivit-b-16x2-kinetics400
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: Vivit-d3
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Vivit-d3
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.2800
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- Accuracy: 0.9509
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- training_steps: 2240
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 3.113 | 0.1 | 224 | 0.2148 | 0.9464 |
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| 3.4763 | 1.1 | 448 | 0.3339 | 0.8869 |
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| 0.5019 | 2.1 | 672 | 0.3398 | 0.9449 |
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| 0.0116 | 3.1 | 896 | 0.3553 | 0.9360 |
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| 0.8144 | 4.1 | 1120 | 0.4592 | 0.9405 |
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| 0.9856 | 5.1 | 1344 | 0.3184 | 0.9286 |
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| 0.0005 | 6.1 | 1568 | 0.2253 | 0.9435 |
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| 0.0001 | 7.1 | 1792 | 0.3713 | 0.9479 |
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| 0.0 | 8.1 | 2016 | 0.3450 | 0.9479 |
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| 0.2266 | 9.1 | 2240 | 0.2800 | 0.9509 |
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### Framework versions
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- Transformers 4.46.2
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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