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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-U12-b-24
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-U12-b-24
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: 0.6456
- 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: 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: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.3806 | 0.4130 |
| 1.379 | 2.0 | 13 | 1.3103 | 0.5435 |
| 1.379 | 2.92 | 19 | 1.2269 | 0.4130 |
| 1.2758 | 4.0 | 26 | 1.1412 | 0.4565 |
| 1.121 | 4.92 | 32 | 1.0650 | 0.4783 |
| 1.121 | 6.0 | 39 | 1.0084 | 0.5217 |
| 0.9871 | 6.92 | 45 | 0.9395 | 0.6522 |
| 0.8612 | 8.0 | 52 | 0.8798 | 0.7174 |
| 0.8612 | 8.92 | 58 | 0.8219 | 0.7391 |
| 0.7653 | 10.0 | 65 | 0.7712 | 0.7826 |
| 0.6674 | 10.92 | 71 | 0.7328 | 0.7609 |
| 0.6674 | 12.0 | 78 | 0.6968 | 0.7391 |
| 0.568 | 12.92 | 84 | 0.6456 | 0.8478 |
| 0.4723 | 14.0 | 91 | 0.6528 | 0.8043 |
| 0.4723 | 14.92 | 97 | 0.7107 | 0.6739 |
| 0.4256 | 16.0 | 104 | 0.6335 | 0.7609 |
| 0.3524 | 16.92 | 110 | 0.5953 | 0.8261 |
| 0.3524 | 18.0 | 117 | 0.5824 | 0.8261 |
| 0.3282 | 18.92 | 123 | 0.6329 | 0.7174 |
| 0.3074 | 20.0 | 130 | 0.5775 | 0.8043 |
| 0.3074 | 20.92 | 136 | 0.5770 | 0.8043 |
| 0.3076 | 22.0 | 143 | 0.5749 | 0.8261 |
| 0.3076 | 22.15 | 144 | 0.5747 | 0.8261 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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