transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder model-index autotrain_compatible endpoints_compatible region:us
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-pretraining-2023_12_11-cxr-classification
  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: 0.6032786885245902
---

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# vit-pretraining-2023_12_11-cxr-classification

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6726
- Accuracy: 0.6033

## 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: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4135        | 1.0   | 228  | 1.2518          | 0.6426   |
| 1.2038        | 2.0   | 457  | 1.2201          | 0.6415   |
| 1.3041        | 3.0   | 685  | 1.1914          | 0.6251   |
| 1.2537        | 4.0   | 914  | 1.1506          | 0.6219   |
| 1.2731        | 5.0   | 1142 | 1.1575          | 0.6175   |
| 1.226         | 6.0   | 1371 | 1.1689          | 0.6262   |
| 1.1369        | 7.0   | 1599 | 1.1385          | 0.6317   |
| 1.0285        | 8.0   | 1828 | 1.1477          | 0.6328   |
| 0.8827        | 9.0   | 2056 | 1.1554          | 0.6262   |
| 0.8787        | 10.0  | 2285 | 1.1614          | 0.6317   |
| 0.7872        | 11.0  | 2513 | 1.1644          | 0.6339   |
| 0.5644        | 12.0  | 2742 | 1.1884          | 0.6361   |
| 0.6407        | 13.0  | 2970 | 1.2171          | 0.5967   |
| 0.4576        | 14.0  | 3199 | 1.2272          | 0.6284   |
| 0.4908        | 15.0  | 3427 | 1.2462          | 0.6186   |
| 0.3767        | 16.0  | 3656 | 1.2672          | 0.6142   |
| 0.2817        | 17.0  | 3884 | 1.3112          | 0.6142   |
| 0.2429        | 18.0  | 4113 | 1.3251          | 0.6109   |
| 0.1766        | 19.0  | 4341 | 1.3853          | 0.6208   |
| 0.2002        | 20.0  | 4570 | 1.4216          | 0.5858   |
| 0.1292        | 21.0  | 4798 | 1.4720          | 0.5934   |
| 0.1414        | 22.0  | 5027 | 1.4895          | 0.6077   |
| 0.1132        | 23.0  | 5255 | 1.5467          | 0.6033   |
| 0.0864        | 24.0  | 5484 | 1.5533          | 0.6120   |
| 0.0748        | 25.0  | 5712 | 1.5990          | 0.6055   |
| 0.0708        | 26.0  | 5941 | 1.6205          | 0.6098   |
| 0.0502        | 27.0  | 6169 | 1.6452          | 0.5978   |
| 0.0505        | 28.0  | 6398 | 1.6634          | 0.5967   |
| 0.0748        | 29.0  | 6626 | 1.6689          | 0.6011   |
| 0.0434        | 29.93 | 6840 | 1.6726          | 0.6033   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
