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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: prove_melanomaprova_melanoma |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8466666666666667 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# prove_melanomaprova_melanoma |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5191 |
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- Accuracy: 0.8467 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.8964 | 0.99 | 31 | 1.0906 | 0.52 | |
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| 0.6588 | 1.98 | 62 | 1.0817 | 0.52 | |
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| 0.6774 | 2.98 | 93 | 0.9474 | 0.52 | |
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| 0.7785 | 4.0 | 125 | 0.8185 | 0.6267 | |
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| 0.6732 | 4.99 | 156 | 0.7531 | 0.7267 | |
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| 0.5438 | 5.98 | 187 | 0.6972 | 0.7333 | |
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| 0.5497 | 6.98 | 218 | 0.6714 | 0.7533 | |
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| 0.4161 | 8.0 | 250 | 0.6440 | 0.7667 | |
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| 0.4968 | 8.99 | 281 | 0.6438 | 0.78 | |
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| 0.5861 | 9.98 | 312 | 0.6266 | 0.7933 | |
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| 0.5182 | 10.98 | 343 | 0.6158 | 0.7867 | |
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| 0.6797 | 12.0 | 375 | 0.6237 | 0.8133 | |
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| 0.622 | 12.99 | 406 | 0.5858 | 0.8333 | |
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| 0.6419 | 13.98 | 437 | 0.5735 | 0.8267 | |
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| 0.3727 | 14.98 | 468 | 0.5641 | 0.8133 | |
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| 0.3822 | 16.0 | 500 | 0.5520 | 0.8267 | |
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| 0.4766 | 16.99 | 531 | 0.5642 | 0.8267 | |
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| 0.4791 | 17.98 | 562 | 0.5309 | 0.8267 | |
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| 0.3918 | 18.98 | 593 | 0.5749 | 0.8267 | |
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| 0.3847 | 20.0 | 625 | 0.5317 | 0.84 | |
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| 0.3722 | 20.99 | 656 | 0.5719 | 0.8267 | |
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| 0.5402 | 21.98 | 687 | 0.5316 | 0.84 | |
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| 0.4358 | 22.98 | 718 | 0.5292 | 0.8333 | |
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| 0.2957 | 24.0 | 750 | 0.5172 | 0.8467 | |
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| 0.4801 | 24.99 | 781 | 0.5376 | 0.84 | |
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| 0.3656 | 25.98 | 812 | 0.5118 | 0.8333 | |
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| 0.3956 | 26.98 | 843 | 0.5081 | 0.8533 | |
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| 0.3343 | 28.0 | 875 | 0.5198 | 0.8533 | |
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| 0.3839 | 28.99 | 906 | 0.5269 | 0.8467 | |
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| 0.4286 | 29.98 | 937 | 0.5163 | 0.8467 | |
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| 0.2736 | 30.98 | 968 | 0.5359 | 0.8333 | |
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| 0.3465 | 32.0 | 1000 | 0.5277 | 0.84 | |
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| 0.4244 | 32.99 | 1031 | 0.5385 | 0.8333 | |
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| 0.308 | 33.98 | 1062 | 0.5141 | 0.8533 | |
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| 0.3494 | 34.98 | 1093 | 0.5129 | 0.8533 | |
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| 0.3851 | 36.0 | 1125 | 0.5199 | 0.84 | |
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| 0.3949 | 36.99 | 1156 | 0.5250 | 0.84 | |
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| 0.3235 | 37.98 | 1187 | 0.5142 | 0.8533 | |
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| 0.3076 | 38.98 | 1218 | 0.5166 | 0.8533 | |
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| 0.3679 | 39.68 | 1240 | 0.5191 | 0.8467 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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