|
--- |
|
license: mit |
|
base_model: google/vivit-b-16x2-kinetics400 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: vivit-b-16x2-kinetics400-0513-O_M |
|
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-kinetics400-0513-O_M |
|
|
|
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: 1.0697 |
|
- Accuracy: 0.805 |
|
|
|
## 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 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- training_steps: 2900 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.5951 | 0.1 | 290 | 1.5856 | 0.45 | |
|
| 1.1484 | 1.1 | 580 | 0.9889 | 0.65 | |
|
| 0.436 | 2.1 | 870 | 0.7230 | 0.77 | |
|
| 0.1011 | 3.1 | 1160 | 1.0218 | 0.78 | |
|
| 0.0631 | 4.1 | 1450 | 1.0562 | 0.805 | |
|
| 0.0005 | 5.1 | 1740 | 1.0855 | 0.805 | |
|
| 0.0004 | 6.1 | 2030 | 1.2053 | 0.785 | |
|
| 0.0005 | 7.1 | 2320 | 1.1131 | 0.8 | |
|
| 0.1483 | 8.1 | 2610 | 1.0447 | 0.81 | |
|
| 0.0013 | 9.1 | 2900 | 1.0697 | 0.805 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
