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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: 0.9338235294117647
---
<!-- 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: 0.2870
- Accuracy: 0.9338
## 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 | 1.7879 | 0.2059 |
| 1.8037 | 1.9487 | 19 | 1.6485 | 0.4044 |
| 1.6961 | 2.9744 | 29 | 1.4882 | 0.3971 |
| 1.5407 | 4.0 | 39 | 1.3069 | 0.5221 |
| 1.3308 | 4.9231 | 48 | 1.1339 | 0.6029 |
| 1.1074 | 5.9487 | 58 | 0.9396 | 0.75 |
| 0.9162 | 6.9744 | 68 | 0.8551 | 0.7647 |
| 0.8174 | 8.0 | 78 | 0.8291 | 0.7574 |
| 0.7135 | 8.9231 | 87 | 0.7505 | 0.7941 |
| 0.6222 | 9.9487 | 97 | 0.6434 | 0.8456 |
| 0.5445 | 10.9744 | 107 | 0.5996 | 0.8529 |
| 0.4935 | 12.0 | 117 | 0.5514 | 0.8529 |
| 0.4131 | 12.9231 | 126 | 0.5029 | 0.8603 |
| 0.4012 | 13.9487 | 136 | 0.5566 | 0.8382 |
| 0.3689 | 14.9744 | 146 | 0.5533 | 0.8382 |
| 0.3533 | 16.0 | 156 | 0.4232 | 0.8971 |
| 0.2954 | 16.9231 | 165 | 0.4589 | 0.8897 |
| 0.2907 | 17.9487 | 175 | 0.4223 | 0.8971 |
| 0.2804 | 18.9744 | 185 | 0.4056 | 0.8971 |
| 0.2469 | 20.0 | 195 | 0.3904 | 0.9118 |
| 0.2643 | 20.9231 | 204 | 0.3866 | 0.9044 |
| 0.2212 | 21.9487 | 214 | 0.4173 | 0.875 |
| 0.2476 | 22.9744 | 224 | 0.6001 | 0.8015 |
| 0.2347 | 24.0 | 234 | 0.3900 | 0.9044 |
| 0.207 | 24.9231 | 243 | 0.4033 | 0.8897 |
| 0.1803 | 25.9487 | 253 | 0.3510 | 0.9265 |
| 0.1979 | 26.9744 | 263 | 0.3723 | 0.9191 |
| 0.1821 | 28.0 | 273 | 0.4320 | 0.8824 |
| 0.1992 | 28.9231 | 282 | 0.3557 | 0.9118 |
| 0.2154 | 29.9487 | 292 | 0.3362 | 0.9191 |
| 0.1801 | 30.9744 | 302 | 0.4358 | 0.875 |
| 0.1794 | 32.0 | 312 | 0.3500 | 0.9191 |
| 0.1566 | 32.9231 | 321 | 0.3046 | 0.9265 |
| 0.1432 | 33.9487 | 331 | 0.3239 | 0.9265 |
| 0.145 | 34.9744 | 341 | 0.3311 | 0.9338 |
| 0.1578 | 36.0 | 351 | 0.3029 | 0.9338 |
| 0.1511 | 36.9231 | 360 | 0.3010 | 0.9338 |
| 0.139 | 37.9487 | 370 | 0.2982 | 0.9265 |
| 0.1294 | 38.9744 | 380 | 0.3261 | 0.9191 |
| 0.1263 | 40.0 | 390 | 0.2932 | 0.9338 |
| 0.1263 | 40.9231 | 399 | 0.2944 | 0.9338 |
| 0.1216 | 41.9487 | 409 | 0.2867 | 0.9338 |
| 0.1199 | 42.9744 | 419 | 0.2887 | 0.9338 |
| 0.128 | 44.0 | 429 | 0.2825 | 0.9338 |
| 0.1115 | 44.9231 | 438 | 0.2880 | 0.9338 |
| 0.1179 | 45.9487 | 448 | 0.2871 | 0.9338 |
| 0.12 | 46.1538 | 450 | 0.2870 | 0.9338 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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