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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vit-cxr4
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
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3774
- Precision: 0.8587
- Recall: 0.9317
- F1: 0.8937
- Accuracy: 0.8924
## 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: 3e-05
- train_batch_size: 96
- eval_batch_size: 64
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3151 | 0.31 | 100 | 0.3317 | 0.8152 | 0.9143 | 0.8619 | 0.8552 |
| 0.319 | 0.63 | 200 | 0.3048 | 0.8670 | 0.8514 | 0.8591 | 0.8620 |
| 0.2926 | 0.94 | 300 | 0.2867 | 0.8580 | 0.8662 | 0.8621 | 0.8631 |
| 0.1884 | 1.25 | 400 | 0.2635 | 0.8468 | 0.9381 | 0.8901 | 0.8856 |
| 0.234 | 1.57 | 500 | 0.2639 | 0.8232 | 0.9677 | 0.8896 | 0.8814 |
| 0.2349 | 1.88 | 600 | 0.2478 | 0.8530 | 0.9328 | 0.8911 | 0.8874 |
| 0.1476 | 2.19 | 700 | 0.2560 | 0.8584 | 0.9297 | 0.8926 | 0.8895 |
| 0.1289 | 2.51 | 800 | 0.2698 | 0.8809 | 0.8916 | 0.8862 | 0.8869 |
| 0.1579 | 2.82 | 900 | 0.2614 | 0.8879 | 0.8715 | 0.8796 | 0.8822 |
| 0.0745 | 3.13 | 1000 | 0.2783 | 0.8854 | 0.8905 | 0.8880 | 0.8889 |
| 0.0697 | 3.45 | 1100 | 0.2844 | 0.8893 | 0.8879 | 0.8886 | 0.8900 |
| 0.0602 | 3.76 | 1200 | 0.3213 | 0.8797 | 0.8932 | 0.8864 | 0.8869 |
| 0.0246 | 4.08 | 1300 | 0.3393 | 0.8753 | 0.9096 | 0.8921 | 0.8913 |
| 0.0301 | 4.39 | 1400 | 0.3593 | 0.8644 | 0.9307 | 0.8964 | 0.8937 |
| 0.0348 | 4.7 | 1500 | 0.3804 | 0.8653 | 0.9344 | 0.8986 | 0.8957 |
| 0.011 | 5.02 | 1600 | 0.3897 | 0.8622 | 0.9365 | 0.8978 | 0.8947 |
| 0.0077 | 5.33 | 1700 | 0.4088 | 0.8754 | 0.9180 | 0.8962 | 0.8950 |
| 0.0064 | 5.64 | 1800 | 0.4281 | 0.8780 | 0.9170 | 0.8971 | 0.8960 |
| 0.0031 | 5.96 | 1900 | 0.4289 | 0.8736 | 0.9207 | 0.8965 | 0.8950 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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