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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: prove_melanomaprova_melanoma
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8466666666666667
---
<!-- 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. -->
# prove_melanomaprova_melanoma
This model is a fine-tuned version of [UnipaPolitoUnimore/vit-large-patch32-384-melanoma](https://huggingface.co/UnipaPolitoUnimore/vit-large-patch32-384-melanoma) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5191
- Accuracy: 0.8467
## 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: 1e-05
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8964 | 0.99 | 31 | 1.0906 | 0.52 |
| 0.6588 | 1.98 | 62 | 1.0817 | 0.52 |
| 0.6774 | 2.98 | 93 | 0.9474 | 0.52 |
| 0.7785 | 4.0 | 125 | 0.8185 | 0.6267 |
| 0.6732 | 4.99 | 156 | 0.7531 | 0.7267 |
| 0.5438 | 5.98 | 187 | 0.6972 | 0.7333 |
| 0.5497 | 6.98 | 218 | 0.6714 | 0.7533 |
| 0.4161 | 8.0 | 250 | 0.6440 | 0.7667 |
| 0.4968 | 8.99 | 281 | 0.6438 | 0.78 |
| 0.5861 | 9.98 | 312 | 0.6266 | 0.7933 |
| 0.5182 | 10.98 | 343 | 0.6158 | 0.7867 |
| 0.6797 | 12.0 | 375 | 0.6237 | 0.8133 |
| 0.622 | 12.99 | 406 | 0.5858 | 0.8333 |
| 0.6419 | 13.98 | 437 | 0.5735 | 0.8267 |
| 0.3727 | 14.98 | 468 | 0.5641 | 0.8133 |
| 0.3822 | 16.0 | 500 | 0.5520 | 0.8267 |
| 0.4766 | 16.99 | 531 | 0.5642 | 0.8267 |
| 0.4791 | 17.98 | 562 | 0.5309 | 0.8267 |
| 0.3918 | 18.98 | 593 | 0.5749 | 0.8267 |
| 0.3847 | 20.0 | 625 | 0.5317 | 0.84 |
| 0.3722 | 20.99 | 656 | 0.5719 | 0.8267 |
| 0.5402 | 21.98 | 687 | 0.5316 | 0.84 |
| 0.4358 | 22.98 | 718 | 0.5292 | 0.8333 |
| 0.2957 | 24.0 | 750 | 0.5172 | 0.8467 |
| 0.4801 | 24.99 | 781 | 0.5376 | 0.84 |
| 0.3656 | 25.98 | 812 | 0.5118 | 0.8333 |
| 0.3956 | 26.98 | 843 | 0.5081 | 0.8533 |
| 0.3343 | 28.0 | 875 | 0.5198 | 0.8533 |
| 0.3839 | 28.99 | 906 | 0.5269 | 0.8467 |
| 0.4286 | 29.98 | 937 | 0.5163 | 0.8467 |
| 0.2736 | 30.98 | 968 | 0.5359 | 0.8333 |
| 0.3465 | 32.0 | 1000 | 0.5277 | 0.84 |
| 0.4244 | 32.99 | 1031 | 0.5385 | 0.8333 |
| 0.308 | 33.98 | 1062 | 0.5141 | 0.8533 |
| 0.3494 | 34.98 | 1093 | 0.5129 | 0.8533 |
| 0.3851 | 36.0 | 1125 | 0.5199 | 0.84 |
| 0.3949 | 36.99 | 1156 | 0.5250 | 0.84 |
| 0.3235 | 37.98 | 1187 | 0.5142 | 0.8533 |
| 0.3076 | 38.98 | 1218 | 0.5166 | 0.8533 |
| 0.3679 | 39.68 | 1240 | 0.5191 | 0.8467 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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