| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: microsoft/swin-base-patch4-window7-224-in22k |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10 |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8358637669265491 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # swin-base-patch4-window7-224-in22k-MM_NMM_Classification_base_V10 |
|
|
| This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4069 |
| - Accuracy: 0.8359 |
| - Auc Overall: 0.9463 |
| - Auc Class 0: 0.9637 |
| - Auc Class 1: 0.9465 |
| - Auc Class 2: 0.9286 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 128 |
| - eval_batch_size: 128 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 512 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 7 |
|
|
| ### Training results |
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| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
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|
