transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: 3-classifier-finetuned-padchest
  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: 0.7250755287009063
---

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# 3-classifier-finetuned-padchest

This model is a fine-tuned version of [nickmuchi/vit-finetuned-chest-xray-pneumonia](https://huggingface.co/nickmuchi/vit-finetuned-chest-xray-pneumonia) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8505
- Accuracy: 0.7251

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0499        | 1.0   | 16   | 1.8761          | 0.3686   |
| 1.704         | 2.0   | 32   | 1.5961          | 0.4955   |
| 1.5393        | 3.0   | 48   | 1.3570          | 0.5770   |
| 1.3161        | 4.0   | 64   | 1.2687          | 0.5770   |
| 1.1991        | 5.0   | 80   | 1.1740          | 0.6073   |
| 1.1459        | 6.0   | 96   | 1.1388          | 0.6073   |
| 1.071         | 7.0   | 112  | 1.0763          | 0.6405   |
| 0.9948        | 8.0   | 128  | 1.0419          | 0.6526   |
| 0.9902        | 9.0   | 144  | 0.9869          | 0.6979   |
| 0.9515        | 10.0  | 160  | 0.9825          | 0.6767   |
| 0.9277        | 11.0  | 176  | 0.9645          | 0.6858   |
| 0.9182        | 12.0  | 192  | 0.9264          | 0.7009   |
| 0.895         | 13.0  | 208  | 0.9138          | 0.6979   |
| 0.8765        | 14.0  | 224  | 0.9089          | 0.7100   |
| 0.8536        | 15.0  | 240  | 0.8941          | 0.7009   |
| 0.8385        | 16.0  | 256  | 0.8764          | 0.7221   |
| 0.8187        | 17.0  | 272  | 0.8659          | 0.7160   |
| 0.8172        | 18.0  | 288  | 0.8673          | 0.7069   |
| 0.8101        | 19.0  | 304  | 0.8530          | 0.7341   |
| 0.8127        | 20.0  | 320  | 0.8505          | 0.7251   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
