transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-384 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vit-cxr4-384
  results: []
---

<!-- 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-cxr4-384

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2413
- Precision: 0.8525
- Recall: 0.9419
- F1: 0.8950
- Accuracy: 0.8926

## 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: 24
- eval_batch_size: 24
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3694        | 0.08  | 100  | 0.3851          | 0.8076    | 0.8435 | 0.8251 | 0.8234   |
| 0.3084        | 0.16  | 200  | 0.4478          | 0.7556    | 0.9889 | 0.8566 | 0.8364   |
| 0.3177        | 0.24  | 300  | 0.3073          | 0.8062    | 0.9572 | 0.8752 | 0.8652   |
| 0.3413        | 0.31  | 400  | 0.2936          | 0.8172    | 0.9434 | 0.8758 | 0.8678   |
| 0.2612        | 0.39  | 500  | 0.2936          | 0.8402    | 0.9122 | 0.8747 | 0.8709   |
| 0.3607        | 0.47  | 600  | 0.2717          | 0.8210    | 0.9603 | 0.8852 | 0.8769   |
| 0.274         | 0.55  | 700  | 0.2875          | 0.8373    | 0.9196 | 0.8765 | 0.8720   |
| 0.3127        | 0.63  | 800  | 0.2664          | 0.8156    | 0.9683 | 0.8854 | 0.8761   |
| 0.2875        | 0.71  | 900  | 0.2643          | 0.8369    | 0.9334 | 0.8825 | 0.8772   |
| 0.2652        | 0.78  | 1000 | 0.2659          | 0.8134    | 0.9683 | 0.8841 | 0.8746   |
| 0.2661        | 0.86  | 1100 | 0.2591          | 0.8334    | 0.9445 | 0.8855 | 0.8793   |
| 0.3019        | 0.94  | 1200 | 0.2729          | 0.8851    | 0.8599 | 0.8723 | 0.8756   |
| 0.229         | 1.02  | 1300 | 0.2548          | 0.8357    | 0.9603 | 0.8937 | 0.8871   |
| 0.1841        | 1.1   | 1400 | 0.2438          | 0.8586    | 0.9217 | 0.8891 | 0.8863   |
| 0.2257        | 1.18  | 1500 | 0.2365          | 0.8629    | 0.9254 | 0.8931 | 0.8905   |
| 0.2217        | 1.25  | 1600 | 0.2509          | 0.8888    | 0.8662 | 0.8773 | 0.8803   |
| 0.2619        | 1.33  | 1700 | 0.2588          | 0.8373    | 0.9582 | 0.8937 | 0.8874   |
| 0.2222        | 1.41  | 1800 | 0.2521          | 0.8644    | 0.9238 | 0.8931 | 0.8908   |
| 0.2044        | 1.49  | 1900 | 0.2598          | 0.8409    | 0.9588 | 0.8960 | 0.8900   |
| 0.2238        | 1.57  | 2000 | 0.2641          | 0.9117    | 0.8302 | 0.8691 | 0.8764   |
| 0.249         | 1.65  | 2100 | 0.2368          | 0.8464    | 0.9561 | 0.8979 | 0.8926   |
| 0.1773        | 1.72  | 2200 | 0.2233          | 0.8682    | 0.9265 | 0.8964 | 0.8942   |
| 0.1447        | 1.8   | 2300 | 0.2269          | 0.8760    | 0.9191 | 0.8970 | 0.8957   |
| 0.245         | 1.88  | 2400 | 0.2355          | 0.8578    | 0.9445 | 0.8991 | 0.8952   |
| 0.1685        | 1.96  | 2500 | 0.2312          | 0.8615    | 0.9344 | 0.8965 | 0.8934   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
