|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: google/vivit-b-16x2-kinetics400 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Vivit-d3 |
|
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-d3 |
|
|
|
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: 0.2800 |
|
- Accuracy: 0.9509 |
|
|
|
## 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: 2240 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 3.113 | 0.1 | 224 | 0.2148 | 0.9464 | |
|
| 3.4763 | 1.1 | 448 | 0.3339 | 0.8869 | |
|
| 0.5019 | 2.1 | 672 | 0.3398 | 0.9449 | |
|
| 0.0116 | 3.1 | 896 | 0.3553 | 0.9360 | |
|
| 0.8144 | 4.1 | 1120 | 0.4592 | 0.9405 | |
|
| 0.9856 | 5.1 | 1344 | 0.3184 | 0.9286 | |
|
| 0.0005 | 6.1 | 1568 | 0.2253 | 0.9435 | |
|
| 0.0001 | 7.1 | 1792 | 0.3713 | 0.9479 | |
|
| 0.0 | 8.1 | 2016 | 0.3450 | 0.9479 | |
|
| 0.2266 | 9.1 | 2240 | 0.2800 | 0.9509 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.2 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.3 |
|
|
