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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-papsmear
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: 0.9411764705882353
---
<!-- 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-in21k-finetuned-papsmear
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2523
- Accuracy: 0.9412
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.6954 | 0.9935 | 38 | 1.6106 | 0.3456 |
| 1.2818 | 1.9869 | 76 | 1.2412 | 0.5735 |
| 1.0023 | 2.9804 | 114 | 0.9875 | 0.7132 |
| 0.7163 | 4.0 | 153 | 0.8399 | 0.6912 |
| 0.5173 | 4.9935 | 191 | 0.6546 | 0.8162 |
| 0.5057 | 5.9869 | 229 | 0.6251 | 0.8309 |
| 0.4313 | 6.9804 | 267 | 0.5696 | 0.8309 |
| 0.325 | 8.0 | 306 | 0.5507 | 0.8309 |
| 0.3811 | 8.9935 | 344 | 0.4429 | 0.8676 |
| 0.2341 | 9.9869 | 382 | 0.4222 | 0.875 |
| 0.3082 | 10.9804 | 420 | 0.6573 | 0.7721 |
| 0.2571 | 12.0 | 459 | 0.4229 | 0.8897 |
| 0.2374 | 12.9935 | 497 | 0.4233 | 0.875 |
| 0.128 | 13.9869 | 535 | 0.3671 | 0.8971 |
| 0.1718 | 14.9804 | 573 | 0.3430 | 0.8971 |
| 0.16 | 16.0 | 612 | 0.4104 | 0.875 |
| 0.1096 | 16.9935 | 650 | 0.2920 | 0.9118 |
| 0.1408 | 17.9869 | 688 | 0.2630 | 0.9044 |
| 0.113 | 18.9804 | 726 | 0.3084 | 0.8824 |
| 0.1272 | 20.0 | 765 | 0.2523 | 0.9412 |
| 0.119 | 20.9935 | 803 | 0.4254 | 0.8824 |
| 0.1068 | 21.9869 | 841 | 0.3519 | 0.8971 |
| 0.0723 | 22.9804 | 879 | 0.3293 | 0.9191 |
| 0.0769 | 24.0 | 918 | 0.2613 | 0.9265 |
| 0.095 | 24.9935 | 956 | 0.2609 | 0.9412 |
| 0.0863 | 25.9869 | 994 | 0.2650 | 0.9265 |
| 0.0795 | 26.9804 | 1032 | 0.2978 | 0.9118 |
| 0.0564 | 28.0 | 1071 | 0.2737 | 0.9191 |
| 0.0562 | 28.9935 | 1109 | 0.2941 | 0.9191 |
| 0.0751 | 29.8039 | 1140 | 0.3111 | 0.9191 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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