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- license: unknown
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ - f1
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+ model-index:
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+ - name: convnext-tiny-224-finetuned-brs
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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: 0.8235294117647058
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+ - name: F1
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+ type: f1
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+ value: 0.7272727272727272
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  ---
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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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+
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+ # convnext-tiny-224-finetuned-brs
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+
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+ This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8667
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+ - Accuracy: 0.8235
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+ - F1: 0.7273
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+ - Precision (ppv): 0.8
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+ - Recall (sensitivity): 0.6667
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+ - Specificity: 0.9091
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+ - Npv: 0.8333
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+ - Auc: 0.7879
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 4
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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: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
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+ | 0.6766 | 6.25 | 100 | 0.7002 | 0.4706 | 0.5263 | 0.3846 | 0.8333 | 0.2727 | 0.75 | 0.5530 |
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+ | 0.6408 | 12.49 | 200 | 0.6770 | 0.6471 | 0.5714 | 0.5 | 0.6667 | 0.6364 | 0.7778 | 0.6515 |
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+ | 0.464 | 18.74 | 300 | 0.6624 | 0.5882 | 0.5882 | 0.4545 | 0.8333 | 0.4545 | 0.8333 | 0.6439 |
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+ | 0.4295 | 24.98 | 400 | 0.6938 | 0.5294 | 0.5 | 0.4 | 0.6667 | 0.4545 | 0.7143 | 0.5606 |
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+ | 0.3952 | 31.25 | 500 | 0.5974 | 0.7059 | 0.6154 | 0.5714 | 0.6667 | 0.7273 | 0.8 | 0.6970 |
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+ | 0.1082 | 37.49 | 600 | 0.6163 | 0.6471 | 0.5 | 0.5 | 0.5 | 0.7273 | 0.7273 | 0.6136 |
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+ | 0.1997 | 43.74 | 700 | 0.6155 | 0.7059 | 0.6154 | 0.5714 | 0.6667 | 0.7273 | 0.8 | 0.6970 |
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+ | 0.1267 | 49.98 | 800 | 0.9063 | 0.6471 | 0.5714 | 0.5 | 0.6667 | 0.6364 | 0.7778 | 0.6515 |
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+ | 0.1178 | 56.25 | 900 | 0.8672 | 0.7059 | 0.6667 | 0.5556 | 0.8333 | 0.6364 | 0.875 | 0.7348 |
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+ | 0.2008 | 62.49 | 1000 | 0.7049 | 0.8235 | 0.7692 | 0.7143 | 0.8333 | 0.8182 | 0.9 | 0.8258 |
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+ | 0.0996 | 68.74 | 1100 | 0.4510 | 0.8235 | 0.7692 | 0.7143 | 0.8333 | 0.8182 | 0.9 | 0.8258 |
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+ | 0.0115 | 74.98 | 1200 | 0.7561 | 0.8235 | 0.7692 | 0.7143 | 0.8333 | 0.8182 | 0.9 | 0.8258 |
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+ | 0.0177 | 81.25 | 1300 | 1.0400 | 0.7059 | 0.6667 | 0.5556 | 0.8333 | 0.6364 | 0.875 | 0.7348 |
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+ | 0.0261 | 87.49 | 1400 | 0.9139 | 0.8235 | 0.7692 | 0.7143 | 0.8333 | 0.8182 | 0.9 | 0.8258 |
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+ | 0.028 | 93.74 | 1500 | 0.7367 | 0.7647 | 0.7143 | 0.625 | 0.8333 | 0.7273 | 0.8889 | 0.7803 |
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+ | 0.0056 | 99.98 | 1600 | 0.8667 | 0.8235 | 0.7273 | 0.8 | 0.6667 | 0.9091 | 0.8333 | 0.7879 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.23.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1