|
|
--- |
|
|
license: apache-2.0 |
|
|
base_model: google/vit-base-patch16-224-in21K |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- imagefolder |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: finetuned-skinpics |
|
|
results: |
|
|
- task: |
|
|
name: Image Classification |
|
|
type: image-classification |
|
|
dataset: |
|
|
name: imagefolder |
|
|
type: imagefolder |
|
|
config: default |
|
|
split: test |
|
|
args: default |
|
|
metrics: |
|
|
- name: Accuracy |
|
|
type: accuracy |
|
|
value: 0.5138888888888888 |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# finetuned-skinpics |
|
|
|
|
|
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 imagefolder dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 1.2540 |
|
|
- Accuracy: 0.5139 |
|
|
|
|
|
## 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: 8 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 7 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
|
| 1.121 | 0.57 | 100 | 1.1020 | 0.2569 | |
|
|
| 1.0768 | 1.15 | 200 | 1.0546 | 0.4792 | |
|
|
| 1.0532 | 1.72 | 300 | 1.0843 | 0.2917 | |
|
|
| 1.0096 | 2.3 | 400 | 1.0693 | 0.4792 | |
|
|
| 1.0716 | 2.87 | 500 | 1.0466 | 0.4931 | |
|
|
| 1.0346 | 3.45 | 600 | 1.0225 | 0.5139 | |
|
|
| 1.0232 | 4.02 | 700 | 1.0230 | 0.4931 | |
|
|
| 0.8936 | 4.6 | 800 | 1.0582 | 0.5069 | |
|
|
| 0.7125 | 5.17 | 900 | 1.0551 | 0.5139 | |
|
|
| 0.6025 | 5.75 | 1000 | 1.1525 | 0.5278 | |
|
|
| 0.4663 | 6.32 | 1100 | 1.2357 | 0.4653 | |
|
|
| 0.5007 | 6.9 | 1200 | 1.2540 | 0.5139 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.39.3 |
|
|
- Pytorch 2.2.1+cu121 |
|
|
- Datasets 2.18.0 |
|
|
- Tokenizers 0.15.2 |
|
|
|
