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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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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: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH-40-30-30-S
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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: Augmented-Final
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split: train
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args: Augmented-Final
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9931339977851605
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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-LungCancer-LC25000-AH-40-30-30-S
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0217
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- Accuracy: 0.9931
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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: 0.0005
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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: 7
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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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| 0.176 | 1.0 | 187 | 0.0891 | 0.9663 |
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| 0.2574 | 2.0 | 374 | 0.2127 | 0.9249 |
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| 0.2416 | 3.0 | 561 | 0.2407 | 0.9236 |
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| 0.2457 | 4.0 | 749 | 0.1245 | 0.9632 |
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| 0.3583 | 5.0 | 936 | 0.1709 | 0.9404 |
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| 0.149 | 6.0 | 1123 | 0.0502 | 0.9814 |
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| 0.061 | 6.99 | 1309 | 0.0217 | 0.9931 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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