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license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-augmentation
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-augmentation
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4287
- Accuracy: 0.8592
- Precision: 0.8580
- Recall: 0.8592
- F1: 0.8574
## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_steps: 770
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9124 | 1.0 | 321 | 0.6025 | 0.7805 | 0.7788 | 0.7805 | 0.7683 |
| 0.5876 | 2.0 | 642 | 0.5819 | 0.7864 | 0.7990 | 0.7864 | 0.7820 |
| 0.5415 | 3.0 | 963 | 0.6149 | 0.8041 | 0.7943 | 0.8041 | 0.7865 |
| 0.4815 | 4.0 | 1284 | 0.4654 | 0.8294 | 0.8259 | 0.8294 | 0.8115 |
| 0.4263 | 5.0 | 1605 | 0.5481 | 0.8259 | 0.8315 | 0.8259 | 0.8023 |
| 0.3515 | 6.0 | 1926 | 0.4287 | 0.8592 | 0.8580 | 0.8592 | 0.8574 |
| 0.3144 | 7.0 | 2247 | 0.5005 | 0.8363 | 0.8320 | 0.8363 | 0.8270 |
| 0.2736 | 8.0 | 2568 | 0.5306 | 0.8294 | 0.8448 | 0.8294 | 0.8302 |
| 0.2519 | 9.0 | 2889 | 0.4733 | 0.8578 | 0.8534 | 0.8578 | 0.8534 |
| 0.2227 | 10.0 | 3210 | 0.4905 | 0.8585 | 0.8520 | 0.8585 | 0.8512 |
| 0.1724 | 11.0 | 3531 | 0.5050 | 0.8655 | 0.8671 | 0.8655 | 0.8628 |
| 0.1596 | 12.0 | 3852 | 0.5263 | 0.8686 | 0.8657 | 0.8686 | 0.8631 |
| 0.1397 | 13.0 | 4173 | 0.7043 | 0.8533 | 0.8703 | 0.8533 | 0.8488 |
| 0.1298 | 14.0 | 4494 | 0.6275 | 0.8679 | 0.8734 | 0.8679 | 0.8632 |
| 0.1029 | 15.0 | 4815 | 0.5564 | 0.8807 | 0.8776 | 0.8807 | 0.8772 |
| 0.0893 | 16.0 | 5136 | 0.5668 | 0.8804 | 0.8823 | 0.8804 | 0.8789 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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