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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
model-index:
- name: chest-beit-base-finetuned
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. -->
# chest-beit-base-finetuned
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2620
- Accuracy: 0.9107
- Precision: 0.8923
- Recall: 0.8923
- F1: 0.8923
## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4775 | 0.99 | 63 | 0.2264 | 0.9142 | 0.8850 | 0.8962 | 0.8903 |
| 0.7117 | 1.99 | 127 | 0.4008 | 0.7391 | 0.3695 | 0.5 | 0.4250 |
| 0.4115 | 3.0 | 191 | 0.4358 | 0.8155 | 0.7871 | 0.8645 | 0.7957 |
| 0.3631 | 4.0 | 255 | 0.3091 | 0.8798 | 0.8381 | 0.8708 | 0.8518 |
| 0.3794 | 4.99 | 318 | 0.2802 | 0.8798 | 0.8393 | 0.8623 | 0.8495 |
| 0.3713 | 5.99 | 382 | 0.2805 | 0.8773 | 0.8371 | 0.8542 | 0.8449 |
| 0.3953 | 7.0 | 446 | 0.3397 | 0.8584 | 0.8185 | 0.8872 | 0.8367 |
| 0.3218 | 8.0 | 510 | 0.3072 | 0.8670 | 0.8257 | 0.8898 | 0.8448 |
| 0.3219 | 8.99 | 573 | 0.2633 | 0.8961 | 0.8582 | 0.8872 | 0.8708 |
| 0.3049 | 9.88 | 630 | 0.2739 | 0.8927 | 0.8528 | 0.8912 | 0.8685 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |