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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: 0.8897058823529411
---
<!-- 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: 0.3853
- Accuracy: 0.8897
## 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: 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: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 9 | 1.7589 | 0.2426 |
| 1.7862 | 1.9487 | 19 | 1.5880 | 0.3824 |
| 1.6727 | 2.9744 | 29 | 1.4212 | 0.4265 |
| 1.5102 | 4.0 | 39 | 1.2241 | 0.5809 |
| 1.3247 | 4.9231 | 48 | 1.0906 | 0.6103 |
| 1.1047 | 5.9487 | 58 | 0.9747 | 0.6765 |
| 0.9405 | 6.9744 | 68 | 0.8745 | 0.7426 |
| 0.823 | 8.0 | 78 | 0.7833 | 0.7426 |
| 0.7244 | 8.9231 | 87 | 0.7160 | 0.7794 |
| 0.6367 | 9.9487 | 97 | 0.7328 | 0.7794 |
| 0.5537 | 10.9744 | 107 | 0.6573 | 0.7868 |
| 0.484 | 12.0 | 117 | 0.5988 | 0.8088 |
| 0.4642 | 12.9231 | 126 | 0.6268 | 0.7941 |
| 0.4166 | 13.9487 | 136 | 0.6549 | 0.7794 |
| 0.4106 | 14.9744 | 146 | 0.5330 | 0.8529 |
| 0.3947 | 16.0 | 156 | 0.5134 | 0.8382 |
| 0.3469 | 16.9231 | 165 | 0.5879 | 0.7794 |
| 0.3151 | 17.9487 | 175 | 0.5683 | 0.8382 |
| 0.2946 | 18.9744 | 185 | 0.5383 | 0.8162 |
| 0.2927 | 20.0 | 195 | 0.5682 | 0.8162 |
| 0.2879 | 20.9231 | 204 | 0.4722 | 0.8603 |
| 0.2512 | 21.9487 | 214 | 0.4806 | 0.8456 |
| 0.2633 | 22.9744 | 224 | 0.4713 | 0.8456 |
| 0.2286 | 24.0 | 234 | 0.5167 | 0.8382 |
| 0.2265 | 24.9231 | 243 | 0.3886 | 0.8824 |
| 0.2107 | 25.9487 | 253 | 0.4396 | 0.8676 |
| 0.2044 | 26.9744 | 263 | 0.4734 | 0.8456 |
| 0.1925 | 28.0 | 273 | 0.4606 | 0.8529 |
| 0.1866 | 28.9231 | 282 | 0.5061 | 0.8309 |
| 0.1928 | 29.9487 | 292 | 0.4202 | 0.8824 |
| 0.1907 | 30.9744 | 302 | 0.5120 | 0.8309 |
| 0.1631 | 32.0 | 312 | 0.4165 | 0.8676 |
| 0.1654 | 32.9231 | 321 | 0.4600 | 0.8676 |
| 0.154 | 33.9487 | 331 | 0.3834 | 0.8971 |
| 0.1459 | 34.9744 | 341 | 0.3686 | 0.8897 |
| 0.1452 | 36.0 | 351 | 0.4174 | 0.8676 |
| 0.1548 | 36.9231 | 360 | 0.3791 | 0.9044 |
| 0.1395 | 37.9487 | 370 | 0.4512 | 0.8529 |
| 0.1333 | 38.9744 | 380 | 0.3775 | 0.8897 |
| 0.1236 | 40.0 | 390 | 0.3666 | 0.8971 |
| 0.1236 | 40.9231 | 399 | 0.3892 | 0.8971 |
| 0.1314 | 41.9487 | 409 | 0.3832 | 0.8897 |
| 0.1322 | 42.9744 | 419 | 0.3919 | 0.8824 |
| 0.1156 | 44.0 | 429 | 0.3699 | 0.8971 |
| 0.1222 | 44.9231 | 438 | 0.3828 | 0.8971 |
| 0.1254 | 45.9487 | 448 | 0.3853 | 0.8897 |
| 0.1129 | 46.1538 | 450 | 0.3853 | 0.8897 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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