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
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: 0.991156462585034
vit-base-patch16-224-finetuned-main-gpu-20e-final
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: 0.0297
- Accuracy: 0.9912
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 |
|---|---|---|---|---|
| 0.4852 | 1.0 | 551 | 0.4533 | 0.8042 |
| 0.3033 | 2.0 | 1102 | 0.2157 | 0.9157 |
| 0.2339 | 3.0 | 1653 | 0.1212 | 0.9534 |
| 0.1694 | 4.0 | 2204 | 0.1076 | 0.9603 |
| 0.1715 | 5.0 | 2755 | 0.0830 | 0.9692 |
| 0.1339 | 6.0 | 3306 | 0.0674 | 0.9762 |
| 0.1527 | 7.0 | 3857 | 0.0556 | 0.9791 |
| 0.1214 | 8.0 | 4408 | 0.0455 | 0.9832 |
| 0.1062 | 9.0 | 4959 | 0.0466 | 0.9829 |
| 0.0974 | 10.0 | 5510 | 0.0403 | 0.9849 |
| 0.0875 | 11.0 | 6061 | 0.0385 | 0.9860 |
| 0.0992 | 12.0 | 6612 | 0.0376 | 0.9870 |
| 0.065 | 13.0 | 7163 | 0.0392 | 0.9864 |
| 0.0775 | 14.0 | 7714 | 0.0344 | 0.9890 |
| 0.0544 | 15.0 | 8265 | 0.0362 | 0.9888 |
| 0.0584 | 16.0 | 8816 | 0.0422 | 0.9872 |
| 0.0722 | 17.0 | 9367 | 0.0314 | 0.9900 |
| 0.0765 | 18.0 | 9918 | 0.0313 | 0.9908 |
| 0.0696 | 19.0 | 10469 | 0.0297 | 0.9912 |
| 0.0596 | 20.0 | 11020 | 0.0285 | 0.9910 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2