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--- |
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license: apache-2.0 |
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base_model: microsoft/swinv2-tiny-patch4-window8-256 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6428571428571429 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# swinv2-tiny-patch4-window8-256-finetuned-gardner-icm-max |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0741 |
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- Accuracy: 0.6429 |
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## Model description |
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Predict Inner Cell Mass Grade - Gardner Score from an embryo image |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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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| 1.0925 | 0.94 | 11 | 1.0631 | 0.7952 | |
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| 0.9552 | 1.96 | 23 | 0.6336 | 0.7952 | |
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| 0.6566 | 2.98 | 35 | 0.5356 | 0.7952 | |
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| 0.5686 | 4.0 | 47 | 0.5150 | 0.7952 | |
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| 0.5703 | 4.94 | 58 | 0.5129 | 0.7952 | |
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| 0.5726 | 5.96 | 70 | 0.5154 | 0.7952 | |
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| 0.5482 | 6.98 | 82 | 0.5142 | 0.7952 | |
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| 0.568 | 8.0 | 94 | 0.5109 | 0.7952 | |
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| 0.5245 | 8.94 | 105 | 0.5134 | 0.7952 | |
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| 0.5979 | 9.96 | 117 | 0.5238 | 0.7952 | |
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| 0.5442 | 10.98 | 129 | 0.5076 | 0.7952 | |
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| 0.545 | 12.0 | 141 | 0.5062 | 0.7952 | |
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| 0.5514 | 12.94 | 152 | 0.5013 | 0.7952 | |
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| 0.5377 | 13.96 | 164 | 0.5045 | 0.7952 | |
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| 0.5282 | 14.98 | 176 | 0.5038 | 0.7952 | |
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| 0.5389 | 16.0 | 188 | 0.4994 | 0.7952 | |
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| 0.5039 | 16.94 | 199 | 0.4996 | 0.7952 | |
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| 0.5348 | 17.96 | 211 | 0.4940 | 0.7952 | |
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| 0.5426 | 18.72 | 220 | 0.4947 | 0.7952 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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