|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- image-classification |
|
- vision |
|
base_model: google/vit-base-patch16-224-in21k |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: skincare-detection |
|
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. --> |
|
|
|
# skincare-detection |
|
|
|
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4840 |
|
- Accuracy: 0.8648 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 12 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 1.3961 | 0.99 | 61 | 0.5629 | 0.7725 | |
|
| 0.4982 | 2.0 | 123 | 0.3991 | 0.8435 | |
|
| 0.3563 | 2.99 | 184 | 0.4330 | 0.8272 | |
|
| 0.2314 | 4.0 | 246 | 0.3969 | 0.8554 | |
|
| 0.1815 | 4.99 | 307 | 0.4492 | 0.8435 | |
|
| 0.1332 | 6.0 | 369 | 0.4474 | 0.8580 | |
|
| 0.0869 | 6.99 | 430 | 0.4520 | 0.8631 | |
|
| 0.0844 | 8.0 | 492 | 0.4469 | 0.8640 | |
|
| 0.0681 | 8.99 | 553 | 0.4533 | 0.8717 | |
|
| 0.0574 | 10.0 | 615 | 0.4952 | 0.8597 | |
|
| 0.0477 | 10.99 | 676 | 0.4772 | 0.8674 | |
|
| 0.0454 | 11.9 | 732 | 0.4840 | 0.8648 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.17.1 |
|
- Tokenizers 0.15.2 |
|
|
