| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: google/vivit-b-16x2-kinetics400 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: ViViT_LSA64_SR_6 |
| | 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_LSA64_SR_6 |
| | |
| | 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: 0.0190 |
| | - Accuracy: 0.9961 |
| | - Precision: 0.9969 |
| | - Recall: 0.9961 |
| | - F1: 0.9960 |
| | |
| | ## 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: 2 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 8 |
| | - 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: 8640 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 15.7817 | 0.0333 | 288 | 2.8824 | 0.4609 | 0.5309 | 0.4609 | 0.4307 | |
| | | 4.7558 | 1.0333 | 576 | 0.5582 | 0.9492 | 0.9586 | 0.9492 | 0.9470 | |
| | | 0.5173 | 2.0333 | 864 | 0.0999 | 0.9805 | 0.9854 | 0.9805 | 0.9798 | |
| | | 0.1244 | 3.0333 | 1152 | 0.0102 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0043 | 4.0333 | 1440 | 0.0265 | 0.9922 | 0.9938 | 0.9922 | 0.9921 | |
| | | 0.021 | 5.0333 | 1728 | 0.0200 | 0.9922 | 0.9938 | 0.9922 | 0.9921 | |
| | | 0.0014 | 6.0333 | 2016 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | | 0.0414 | 7.0333 | 2304 | 0.0075 | 0.9961 | 0.9969 | 0.9961 | 0.9960 | |
| | | 0.0386 | 8.0333 | 2592 | 0.0190 | 0.9961 | 0.9969 | 0.9961 | 0.9960 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.46.1 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.1.0 |
| | - Tokenizers 0.20.1 |
| | |
