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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-RU5-10
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: 0.7333333333333333
---
<!-- 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-RU5-10
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: 0.8095
- Accuracy: 0.7333
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 9 | 1.2939 | 0.4667 |
| 1.3501 | 1.95 | 19 | 1.1706 | 0.5833 |
| 1.2272 | 2.97 | 29 | 1.0594 | 0.6333 |
| 1.0941 | 4.0 | 39 | 0.9773 | 0.6 |
| 0.979 | 4.92 | 48 | 0.9142 | 0.6833 |
| 0.8694 | 5.95 | 58 | 0.8569 | 0.7 |
| 0.7662 | 6.97 | 68 | 0.8364 | 0.6833 |
| 0.7002 | 8.0 | 78 | 0.8071 | 0.7 |
| 0.6443 | 8.92 | 87 | 0.8095 | 0.7333 |
| 0.629 | 9.23 | 90 | 0.8134 | 0.7167 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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