metadata
library_name: transformers
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-ucf101-subset
results: []
vivit-b-16x2-kinetics400-finetuned-ucf101-subset
This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6545
- Accuracy: 0.8339
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 7060
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1788 | 0.0494 | 349 | 1.1337 | 0.5608 |
| 0.93 | 1.0494 | 698 | 1.0407 | 0.6386 |
| 0.7623 | 2.0494 | 1047 | 0.8954 | 0.6775 |
| 0.6178 | 3.0494 | 1396 | 0.8011 | 0.7050 |
| 0.4934 | 4.0494 | 1745 | 0.8328 | 0.7034 |
| 0.5447 | 5.0494 | 2094 | 0.7323 | 0.7731 |
| 0.4106 | 6.0494 | 2443 | 0.6595 | 0.7763 |
| 0.4948 | 7.0494 | 2792 | 0.6986 | 0.7780 |
| 0.3257 | 8.0494 | 3141 | 0.5867 | 0.8152 |
| 0.2109 | 9.0494 | 3490 | 0.6624 | 0.8039 |
| 0.3482 | 10.0494 | 3839 | 0.6958 | 0.8104 |
| 0.4104 | 11.0494 | 4188 | 0.5619 | 0.8331 |
| 0.2176 | 12.0494 | 4537 | 0.5727 | 0.8217 |
| 0.1634 | 13.0494 | 4886 | 0.4984 | 0.8606 |
| 0.1256 | 14.0494 | 5235 | 0.5515 | 0.8444 |
| 0.188 | 15.0494 | 5584 | 0.5470 | 0.8428 |
| 0.2174 | 16.0494 | 5933 | 0.4847 | 0.8590 |
| 0.1619 | 17.0494 | 6282 | 0.5030 | 0.8784 |
| 0.3518 | 18.0494 | 6631 | 0.4783 | 0.8784 |
| 0.1533 | 19.0494 | 6980 | 0.5133 | 0.8703 |
| 0.101 | 20.0113 | 7060 | 0.5090 | 0.8703 |
Framework versions
- Transformers 4.44.1
- Pytorch 2.0.1+cu117
- Datasets 2.21.0
- Tokenizers 0.19.1