---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-pretraining-2024_04_02-atelectasis-classifier
  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: 0.7644320297951583
---

<!-- 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-pretraining-2024_04_02-atelectasis-classifier

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5020
- Accuracy: 0.7644

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6304        | 1.0   | 537   | 0.6342          | 0.6709   |
| 0.5931        | 2.0   | 1074  | 0.5669          | 0.7207   |
| 0.5027        | 3.0   | 1611  | 0.5397          | 0.7393   |
| 0.5659        | 4.0   | 2148  | 0.5341          | 0.7458   |
| 0.5115        | 5.0   | 2685  | 0.5433          | 0.7346   |
| 0.5108        | 6.0   | 3222  | 0.5454          | 0.7309   |
| 0.5187        | 7.0   | 3759  | 0.5136          | 0.7621   |
| 0.4435        | 8.0   | 4296  | 0.5057          | 0.7677   |
| 0.583         | 9.0   | 4833  | 0.5042          | 0.7584   |
| 0.5256        | 10.0  | 5370  | 0.5249          | 0.7495   |
| 0.4818        | 11.0  | 5907  | 0.5212          | 0.7481   |
| 0.5575        | 12.0  | 6444  | 0.5061          | 0.7481   |
| 0.3572        | 13.0  | 6981  | 0.5042          | 0.7602   |
| 0.489         | 14.0  | 7518  | 0.5004          | 0.7709   |
| 0.4773        | 15.0  | 8055  | 0.5074          | 0.7700   |
| 0.4577        | 16.0  | 8592  | 0.5054          | 0.7677   |
| 0.4619        | 17.0  | 9129  | 0.5021          | 0.7686   |
| 0.3865        | 18.0  | 9666  | 0.5074          | 0.7644   |
| 0.4889        | 19.0  | 10203 | 0.5113          | 0.7598   |
| 0.4637        | 20.0  | 10740 | 0.5020          | 0.7644   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2