| --- |
| library_name: transformers |
| license: mit |
| base_model: google/vivit-b-16x2-kinetics400 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: VIVIT-d2 |
| 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-d2 |
|
|
| 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: 2.9103 |
| - Accuracy: 0.4210 |
|
|
| ## 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: 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: 6650 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 2.556 | 0.1 | 665 | 2.3470 | 0.2123 | |
| | 2.0142 | 1.1 | 1330 | 2.1601 | 0.3180 | |
| | 2.122 | 2.1 | 1995 | 2.0851 | 0.4047 | |
| | 1.7405 | 3.1 | 2660 | 2.3452 | 0.4205 | |
| | 1.2998 | 4.1 | 3325 | 2.3814 | 0.4557 | |
| | 1.4591 | 5.1 | 3990 | 2.7093 | 0.3820 | |
| | 0.8984 | 6.1 | 4655 | 2.5562 | 0.3584 | |
| | 0.3971 | 7.1 | 5320 | 3.1583 | 0.4057 | |
| | 0.5996 | 8.1 | 5985 | 2.9134 | 0.4154 | |
| | 0.8684 | 9.1 | 6650 | 2.9103 | 0.4210 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.2 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
| |
