| | --- |
| | license: apache-2.0 |
| | base_model: microsoft/swinv2-tiny-patch4-window8-256 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: swinv2-tiny-patch4-window8-256-finetuned-gardner-exp-max |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: imagefolder |
| | type: imagefolder |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.8389261744966443 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # swinv2-tiny-patch4-window8-256-finetuned-gardner-exp-max |
| |
|
| | This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5312 |
| | - Accuracy: 0.8389 |
| |
|
| | ## Model description |
| |
|
| | Predict Expansion Grade - Gardner Score from an embryo image |
| |
|
| | ## 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: 25 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.6068 | 0.97 | 14 | 1.5809 | 0.5415 | |
| | | 1.56 | 2.0 | 29 | 1.2830 | 0.5415 | |
| | | 1.1852 | 2.97 | 43 | 1.0794 | 0.5415 | |
| | | 1.1132 | 4.0 | 58 | 0.9314 | 0.6488 | |
| | | 0.9416 | 4.97 | 72 | 0.8935 | 0.6341 | |
| | | 0.9143 | 6.0 | 87 | 0.8009 | 0.6829 | |
| | | 0.8243 | 6.97 | 101 | 0.8067 | 0.6634 | |
| | | 0.8171 | 8.0 | 116 | 0.7783 | 0.6780 | |
| | | 0.7901 | 8.97 | 130 | 0.7871 | 0.6585 | |
| | | 0.7944 | 10.0 | 145 | 0.7414 | 0.6976 | |
| | | 0.7669 | 10.97 | 159 | 0.6977 | 0.7122 | |
| | | 0.7478 | 12.0 | 174 | 0.7043 | 0.7122 | |
| | | 0.766 | 12.97 | 188 | 0.7778 | 0.6585 | |
| | | 0.7322 | 14.0 | 203 | 0.7504 | 0.6780 | |
| | | 0.7242 | 14.97 | 217 | 0.7291 | 0.6829 | |
| | | 0.7554 | 16.0 | 232 | 0.7694 | 0.6634 | |
| | | 0.7422 | 16.97 | 246 | 0.7569 | 0.6829 | |
| | | 0.7292 | 18.0 | 261 | 0.7389 | 0.6780 | |
| | | 0.7354 | 18.97 | 275 | 0.6684 | 0.7122 | |
| | | 0.6847 | 20.0 | 290 | 0.6821 | 0.7122 | |
| | | 0.7231 | 20.97 | 304 | 0.6839 | 0.7024 | |
| | | 0.6962 | 22.0 | 319 | 0.6958 | 0.6878 | |
| | | 0.7079 | 22.97 | 333 | 0.7039 | 0.6878 | |
| | | 0.7088 | 24.0 | 348 | 0.6974 | 0.6878 | |
| | | 0.7106 | 24.14 | 350 | 0.6975 | 0.6878 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.2 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.16.0 |
| | - Tokenizers 0.15.0 |
| |
|
