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README.md
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---
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license: apache-2.0
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base_model: facebook/convnextv2-large-1k-224
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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: convnextv2-large-1k-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30-Shuffled
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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: 0.9029900332225914
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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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# convnextv2-large-1k-224-finetuned-LungCancer-Classification-LC25000-AH-40-30-30-Shuffled
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This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3298
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- Accuracy: 0.9030
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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: 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: 0.5
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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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| 0.2522 | 0.99 | 93 | 0.1288 | 0.9623 |
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| 0.1579 | 1.99 | 187 | 0.1211 | 0.9573 |
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| 1.1016 | 3.0 | 281 | 1.1018 | 0.3216 |
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| 1.0934 | 4.0 | 375 | 1.0787 | 0.6432 |
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| 0.5795 | 4.99 | 468 | 0.5864 | 0.6445 |
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| 0.5437 | 5.99 | 562 | 0.5733 | 0.7369 |
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| 0.3369 | 6.94 | 651 | 0.3298 | 0.9030 |
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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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