metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-main-gpu-20e-final-1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9917517006802721
vit-base-patch16-224-finetuned-main-gpu-20e-final-1
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0272
- Accuracy: 0.9918
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: 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4776 | 1.0 | 551 | 0.4399 | 0.8125 |
| 0.3207 | 2.0 | 1102 | 0.2645 | 0.8978 |
| 0.2292 | 3.0 | 1653 | 0.1388 | 0.9468 |
| 0.1811 | 4.0 | 2204 | 0.0943 | 0.9662 |
| 0.1633 | 5.0 | 2755 | 0.0740 | 0.9723 |
| 0.1355 | 6.0 | 3306 | 0.0744 | 0.9727 |
| 0.1413 | 7.0 | 3857 | 0.0548 | 0.9813 |
| 0.1257 | 8.0 | 4408 | 0.0442 | 0.9844 |
| 0.1057 | 9.0 | 4959 | 0.0517 | 0.9821 |
| 0.1 | 10.0 | 5510 | 0.0376 | 0.9868 |
| 0.0873 | 11.0 | 6061 | 0.0410 | 0.9866 |
| 0.0974 | 12.0 | 6612 | 0.0430 | 0.9861 |
| 0.0673 | 13.0 | 7163 | 0.0421 | 0.9852 |
| 0.0913 | 14.0 | 7714 | 0.0339 | 0.9882 |
| 0.0594 | 15.0 | 8265 | 0.0327 | 0.9896 |
| 0.0608 | 16.0 | 8816 | 0.0379 | 0.9885 |
| 0.0725 | 17.0 | 9367 | 0.0288 | 0.9904 |
| 0.0742 | 18.0 | 9918 | 0.0284 | 0.9906 |
| 0.0708 | 19.0 | 10469 | 0.0273 | 0.9916 |
| 0.0648 | 20.0 | 11020 | 0.0272 | 0.9918 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2