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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U13b-80RX
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: 0.8478260869565217
---
<!-- 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-base-patch16-224-ve-U13b-80RX
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6099
- Accuracy: 0.8478
## 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: 5.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3857 | 0.99 | 17 | 1.3703 | 0.5652 |
| 1.3134 | 1.98 | 34 | 1.2235 | 0.4565 |
| 1.0384 | 2.97 | 51 | 1.0173 | 0.5435 |
| 0.908 | 3.96 | 68 | 0.8346 | 0.7826 |
| 0.75 | 4.95 | 85 | 0.7343 | 0.7826 |
| 0.5131 | 6.0 | 103 | 0.6099 | 0.8478 |
| 0.395 | 6.99 | 120 | 0.5932 | 0.7826 |
| 0.355 | 7.98 | 137 | 0.7209 | 0.7391 |
| 0.2658 | 8.97 | 154 | 0.5652 | 0.8043 |
| 0.248 | 9.96 | 171 | 0.7103 | 0.7826 |
| 0.2086 | 10.95 | 188 | 0.6788 | 0.7609 |
| 0.1532 | 12.0 | 206 | 0.5725 | 0.7826 |
| 0.147 | 12.99 | 223 | 0.6130 | 0.8043 |
| 0.1145 | 13.98 | 240 | 0.6563 | 0.8043 |
| 0.1053 | 14.97 | 257 | 0.5993 | 0.8043 |
| 0.0971 | 15.96 | 274 | 0.8840 | 0.7391 |
| 0.0947 | 16.95 | 291 | 0.6256 | 0.8043 |
| 0.1055 | 18.0 | 309 | 0.8406 | 0.7609 |
| 0.0974 | 18.99 | 326 | 0.6355 | 0.8478 |
| 0.1215 | 19.98 | 343 | 0.6651 | 0.8043 |
| 0.108 | 20.97 | 360 | 0.8301 | 0.7826 |
| 0.0784 | 21.96 | 377 | 0.8837 | 0.7609 |
| 0.0919 | 22.95 | 394 | 0.6985 | 0.8043 |
| 0.064 | 24.0 | 412 | 0.6426 | 0.8043 |
| 0.0669 | 24.99 | 429 | 0.8102 | 0.7826 |
| 0.0878 | 25.98 | 446 | 0.7863 | 0.7391 |
| 0.0875 | 26.97 | 463 | 0.8777 | 0.7609 |
| 0.0441 | 27.96 | 480 | 0.7324 | 0.8043 |
| 0.088 | 28.95 | 497 | 0.8099 | 0.7826 |
| 0.0739 | 30.0 | 515 | 0.7776 | 0.8043 |
| 0.0598 | 30.99 | 532 | 0.8188 | 0.7826 |
| 0.0443 | 31.98 | 549 | 0.8549 | 0.8043 |
| 0.0376 | 32.97 | 566 | 0.8049 | 0.7826 |
| 0.0375 | 33.96 | 583 | 0.8037 | 0.8043 |
| 0.0346 | 34.95 | 600 | 0.8255 | 0.8261 |
| 0.0471 | 36.0 | 618 | 0.8239 | 0.8043 |
| 0.0669 | 36.99 | 635 | 0.8188 | 0.8043 |
| 0.0438 | 37.98 | 652 | 0.8443 | 0.8043 |
| 0.0549 | 38.97 | 669 | 0.8551 | 0.8043 |
| 0.0622 | 39.61 | 680 | 0.8551 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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