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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-chest-xray
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-base-chest-xray
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the trpakov/chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0856
- Accuracy: 0.9742
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1891 | 0.1307 | 100 | 0.1028 | 0.9665 |
| 0.2123 | 0.2614 | 200 | 0.1254 | 0.9562 |
| 0.0536 | 0.3922 | 300 | 0.1142 | 0.9691 |
| 0.0799 | 0.5229 | 400 | 0.1173 | 0.9648 |
| 0.0537 | 0.6536 | 500 | 0.0856 | 0.9742 |
| 0.0911 | 0.7843 | 600 | 0.2005 | 0.9425 |
| 0.1027 | 0.9150 | 700 | 0.0869 | 0.9708 |
| 0.1011 | 1.0458 | 800 | 0.1063 | 0.9631 |
| 0.0717 | 1.1765 | 900 | 0.1424 | 0.9588 |
| 0.0605 | 1.3072 | 1000 | 0.1525 | 0.9648 |
| 0.0573 | 1.4379 | 1100 | 0.0970 | 0.9700 |
| 0.024 | 1.5686 | 1200 | 0.0867 | 0.9751 |
| 0.0056 | 1.6993 | 1300 | 0.0888 | 0.9760 |
| 0.0051 | 1.8301 | 1400 | 0.1054 | 0.9768 |
| 0.063 | 1.9608 | 1500 | 0.1896 | 0.9571 |
| 0.002 | 2.0915 | 1600 | 0.1886 | 0.9588 |
| 0.005 | 2.2222 | 1700 | 0.1184 | 0.9734 |
| 0.0083 | 2.3529 | 1800 | 0.1084 | 0.9760 |
| 0.0013 | 2.4837 | 1900 | 0.0903 | 0.9777 |
| 0.0298 | 2.6144 | 2000 | 0.1023 | 0.9734 |
| 0.0008 | 2.7451 | 2100 | 0.1104 | 0.9768 |
| 0.0011 | 2.8758 | 2200 | 0.1128 | 0.9785 |
| 0.0006 | 3.0065 | 2300 | 0.1395 | 0.9734 |
| 0.0059 | 3.1373 | 2400 | 0.1419 | 0.9725 |
| 0.0005 | 3.2680 | 2500 | 0.1335 | 0.9777 |
| 0.0005 | 3.3987 | 2600 | 0.1249 | 0.9768 |
| 0.0007 | 3.5294 | 2700 | 0.1157 | 0.9777 |
| 0.0005 | 3.6601 | 2800 | 0.1202 | 0.9785 |
| 0.001 | 3.7908 | 2900 | 0.1239 | 0.9777 |
| 0.0004 | 3.9216 | 3000 | 0.1231 | 0.9768 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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