| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - mnist |
| metrics: |
| - accuracy |
| model-index: |
| - name: image-classification |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: mnist |
| type: mnist |
| args: mnist |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9833333333333333 |
| - task: |
| type: image-classification |
| name: Image Classification |
| dataset: |
| name: mnist |
| type: mnist |
| config: mnist |
| split: test |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9837 |
| verified: true |
| - name: Precision Macro |
| type: precision |
| value: 0.9836633320435293 |
| verified: true |
| - name: Precision Micro |
| type: precision |
| value: 0.9837 |
| verified: true |
| - name: Precision Weighted |
| type: precision |
| value: 0.9837581874425055 |
| verified: true |
| - name: Recall Macro |
| type: recall |
| value: 0.9831030184134061 |
| verified: true |
| - name: Recall Micro |
| type: recall |
| value: 0.9837 |
| verified: true |
| - name: Recall Weighted |
| type: recall |
| value: 0.9837 |
| verified: true |
| - name: F1 Macro |
| type: f1 |
| value: 0.983311507665402 |
| verified: true |
| - name: F1 Micro |
| type: f1 |
| value: 0.9837 |
| verified: true |
| - name: F1 Weighted |
| type: f1 |
| value: 0.9836627364250822 |
| verified: true |
| - name: loss |
| type: loss |
| value: 0.051053039729595184 |
| verified: true |
| - name: matthews_correlation |
| type: matthews_correlation |
| value: 0.9818945021449504 |
| verified: true |
| --- |
| |
| <!-- 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. --> |
|
|
| # image-classification |
|
|
| This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the mnist dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0556 |
| - Accuracy: 0.9833 |
|
|
| ## 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: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.3743 | 1.0 | 422 | 0.0556 | 0.9833 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.20.0 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.3.2 |
| - Tokenizers 0.12.1 |
|
|
