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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: facebook/deit-base-patch16-224
model-index:
- name: chest-deit-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-deit-base-finetuned
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0979
- Accuracy: 0.9622
- Precision: 0.9531
- Recall: 0.9562
- F1: 0.9546
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2556 | 0.99 | 63 | 0.2019 | 0.9185 | 0.9446 | 0.8469 | 0.8822 |
| 0.2302 | 1.99 | 127 | 0.1098 | 0.9614 | 0.9396 | 0.9654 | 0.9514 |
| 0.2258 | 3.0 | 191 | 0.1151 | 0.9622 | 0.9641 | 0.9372 | 0.9496 |
| 0.1465 | 4.0 | 255 | 0.0733 | 0.9725 | 0.9653 | 0.9633 | 0.9643 |
| 0.1763 | 4.99 | 318 | 0.0763 | 0.9725 | 0.9703 | 0.9580 | 0.9639 |
| 0.1627 | 5.99 | 382 | 0.1057 | 0.9571 | 0.9315 | 0.9656 | 0.9466 |
| 0.1509 | 7.0 | 446 | 0.0701 | 0.9751 | 0.9638 | 0.9725 | 0.9680 |
| 0.1209 | 8.0 | 510 | 0.1047 | 0.9571 | 0.9315 | 0.9656 | 0.9466 |
| 0.0961 | 8.99 | 573 | 0.0721 | 0.9734 | 0.9577 | 0.9756 | 0.9662 |
| 0.1063 | 9.88 | 630 | 0.0885 | 0.9622 | 0.9398 | 0.9681 | 0.9526 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |