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Nicole-M
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Dataset1-SwinV2

Image Classification
Safetensors
swinv2
breast cancer
Generated from Trainer
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SwinV2-finetuned

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  1. README.md +21 -16
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
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  ---
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- license: apache-2.0
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- base_model: microsoft/swinv2-base-patch4-window8-256
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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-Mammmogram-V1
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  results: []
@@ -15,10 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # swinV2-Mammmogram-V1
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- This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1057
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- - Accuracy: 0.9691
 
 
 
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  ## Model description
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@@ -43,24 +50,22 @@ The following hyperparameters were used during training:
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  - seed: 42
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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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- - num_epochs: 6
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- - mixed_precision_training: Native AMP
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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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- | 0.549 | 1.0 | 1823 | 0.5326 | 0.7283 |
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- | 0.3398 | 2.0 | 3646 | 0.4005 | 0.7953 |
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- | 0.1833 | 3.0 | 5469 | 0.2375 | 0.8993 |
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- | 0.1374 | 4.0 | 7292 | 0.1703 | 0.9291 |
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- | 0.1574 | 5.0 | 9115 | 0.1146 | 0.9625 |
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- | 0.1222 | 6.0 | 10938 | 0.1057 | 0.9691 |
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  ### Framework versions
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  - Transformers 4.42.4
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  - Pytorch 2.3.1+cu121
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- - Datasets 2.20.0
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  - Tokenizers 0.19.1
 
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  ---
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+ base_model: swinv2
 
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  tags:
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+ - image-classification
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+ - breast cancer
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - precision
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+ - recall
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+ - f1
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  model-index:
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  - name: swinV2-Mammmogram-V1
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  results: []
 
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  # swinV2-Mammmogram-V1
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+ This model is a fine-tuned version of [swinv2](https://huggingface.co/swinv2) on the Mammogram V1 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1434
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+ - Accuracy: 0.9524
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+ - Precision: 0.9751
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+ - Recall: 0.9524
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+ - F1: 0.9630
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  ## Model description
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  - seed: 42
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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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+ - num_epochs: 5
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.4455 | 1.0 | 1112 | 0.1385 | 0.9782 | 0.9739 | 0.9782 | 0.9760 |
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+ | 0.3974 | 2.0 | 2224 | 0.1974 | 0.9524 | 0.9749 | 0.9524 | 0.9630 |
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+ | 0.3712 | 3.0 | 3336 | 0.1386 | 0.9735 | 0.9748 | 0.9735 | 0.9741 |
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+ | 0.2748 | 4.0 | 4448 | 0.1597 | 0.9479 | 0.9752 | 0.9479 | 0.9607 |
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+ | 0.2603 | 5.0 | 5560 | 0.1434 | 0.9524 | 0.9751 | 0.9524 | 0.9630 |
 
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  ### Framework versions
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  - Transformers 4.42.4
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  - Pytorch 2.3.1+cu121
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+ - Datasets 2.21.0
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  - Tokenizers 0.19.1
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