|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: vit-focal-skin |
|
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. --> |
|
|
|
# vit-focal-skin |
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5783 |
|
- Accuracy: 0.8653 |
|
- F1: 0.8726 |
|
- Precision: 0.8851 |
|
- Recall: 0.8653 |
|
|
|
## 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: 0.0002 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 6 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
|
| 0.1024 | 1.0 | 626 | 0.4875 | 0.8187 | 0.8247 | 0.8725 | 0.8187 | |
|
| 0.1279 | 2.0 | 1252 | 0.4645 | 0.8187 | 0.8223 | 0.8295 | 0.8187 | |
|
| 0.0912 | 3.0 | 1878 | 0.4883 | 0.8497 | 0.8454 | 0.8462 | 0.8497 | |
|
| 0.0397 | 4.0 | 2504 | 0.5439 | 0.8238 | 0.8274 | 0.8342 | 0.8238 | |
|
| 0.0004 | 5.0 | 3130 | 0.5795 | 0.8601 | 0.8668 | 0.8787 | 0.8601 | |
|
| 0.0002 | 6.0 | 3756 | 0.5783 | 0.8653 | 0.8726 | 0.8851 | 0.8653 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.2 |
|
- Pytorch 1.13.1 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
