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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
base_model: facebook/deit-base-patch16-224
model-index:
- name: organc-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. -->
# organc-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 the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2795
- Accuracy: 0.9240
- Precision: 0.9199
- Recall: 0.9123
- F1: 0.9154
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7947 | 1.0 | 203 | 0.3123 | 0.8976 | 0.9090 | 0.8450 | 0.8632 |
| 0.6703 | 2.0 | 406 | 0.1400 | 0.9607 | 0.9590 | 0.9543 | 0.9535 |
| 0.5941 | 3.0 | 609 | 0.1182 | 0.9699 | 0.9647 | 0.9681 | 0.9649 |
| 0.5837 | 4.0 | 813 | 0.1016 | 0.9678 | 0.9558 | 0.9586 | 0.9551 |
| 0.5193 | 5.0 | 1016 | 0.0800 | 0.9791 | 0.9701 | 0.9684 | 0.9675 |
| 0.5513 | 6.0 | 1219 | 0.0579 | 0.9862 | 0.9831 | 0.9855 | 0.9840 |
| 0.4343 | 7.0 | 1422 | 0.0775 | 0.9833 | 0.9858 | 0.9818 | 0.9835 |
| 0.3942 | 8.0 | 1626 | 0.0782 | 0.9833 | 0.9813 | 0.9827 | 0.9817 |
| 0.2971 | 9.0 | 1829 | 0.0839 | 0.9862 | 0.9884 | 0.9866 | 0.9873 |
| 0.3242 | 9.99 | 2030 | 0.0745 | 0.9870 | 0.9877 | 0.9863 | 0.9868 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |