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README.md
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---
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license: apache-2.0
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base_model: microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3
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results: []
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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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# swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-Lesion-Classification-HAM10000-3
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This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0112
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- Accuracy: 0.9951
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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: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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: 0.5
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- num_epochs: 10
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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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| 1.1885 | 1.0 | 114 | 0.8718 | 0.6593 |
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| 0.7037 | 2.0 | 228 | 0.4208 | 0.8637 |
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| 0.5085 | 2.99 | 342 | 0.3446 | 0.8744 |
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| 0.2874 | 4.0 | 457 | 0.2027 | 0.9327 |
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| 0.355 | 5.0 | 571 | 0.1666 | 0.9401 |
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| 0.2493 | 6.0 | 685 | 0.0969 | 0.9655 |
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| 0.1909 | 6.99 | 799 | 0.0558 | 0.9836 |
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| 0.1821 | 8.0 | 914 | 0.0412 | 0.9901 |
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| 0.1853 | 9.0 | 1028 | 0.0239 | 0.9943 |
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| 0.0666 | 9.98 | 1140 | 0.0112 | 0.9951 |
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### Framework versions
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- Transformers 4.32.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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