|
--- |
|
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: [] |
|
--- |
|
|
|
<!-- 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-b-16x2-kinetics400-finetuned-ucf101-subset |
|
|
|
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: 0.6545 |
|
- 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 |
|
|
