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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-patch16-224-13_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.835
---
<!-- 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-patch16-224-13_model
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: 0.5185
- Accuracy: 0.835
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7535 | 0.9787 | 23 | 1.3773 | 0.545 |
| 0.9606 | 2.0 | 47 | 1.1264 | 0.625 |
| 0.5199 | 2.9787 | 70 | 0.7703 | 0.705 |
| 0.3037 | 4.0 | 94 | 0.6922 | 0.745 |
| 0.1607 | 4.9787 | 117 | 0.5718 | 0.81 |
| 0.148 | 6.0 | 141 | 0.5436 | 0.82 |
| 0.1238 | 6.9787 | 164 | 0.5454 | 0.805 |
| 0.0889 | 8.0 | 188 | 0.5023 | 0.84 |
| 0.0745 | 8.8085 | 207 | 0.5185 | 0.835 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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