---
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