| | --- |
| | 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-te-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: 0.594017094017094 |
| | --- |
| | |
| | <!-- 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-te-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: 0.8795 |
| | - Accuracy: 0.5940 |
| |
|
| | ## Model description |
| |
|
| | Predict Trophectoderm 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: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.0943 | 0.94 | 11 | 1.0750 | 0.6325 | |
| | | 0.9996 | 1.96 | 23 | 0.8011 | 0.6325 | |
| | | 0.7731 | 2.98 | 35 | 0.7182 | 0.6325 | |
| | | 0.7564 | 4.0 | 47 | 0.7109 | 0.6325 | |
| | | 0.7331 | 4.94 | 58 | 0.7026 | 0.6325 | |
| | | 0.7336 | 5.96 | 70 | 0.6848 | 0.6325 | |
| | | 0.7305 | 6.98 | 82 | 0.6938 | 0.6325 | |
| | | 0.7314 | 8.0 | 94 | 0.6549 | 0.6325 | |
| | | 0.6905 | 8.94 | 105 | 0.6364 | 0.6867 | |
| | | 0.7315 | 9.96 | 117 | 0.6223 | 0.6687 | |
| | | 0.6839 | 10.98 | 129 | 0.6528 | 0.7530 | |
| | | 0.6931 | 12.0 | 141 | 0.6209 | 0.7410 | |
| | | 0.6705 | 12.94 | 152 | 0.6296 | 0.7169 | |
| | | 0.7227 | 13.96 | 164 | 0.6039 | 0.7108 | |
| | | 0.6695 | 14.98 | 176 | 0.6049 | 0.7530 | |
| | | 0.6981 | 16.0 | 188 | 0.5965 | 0.7048 | |
| | | 0.6566 | 16.94 | 199 | 0.6111 | 0.7410 | |
| | | 0.6828 | 17.96 | 211 | 0.5969 | 0.7530 | |
| | | 0.6632 | 18.72 | 220 | 0.5947 | 0.7530 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.2 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.16.0 |
| | - Tokenizers 0.15.0 |
| |
|
