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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-exp-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: 0.8154362416107382 |
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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-exp-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: 0.6445 |
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- Accuracy: 0.8154 |
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## Model description |
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Predict Expansion Grade - Gardner Score from an embryo image |
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## Intended uses & limitations |
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More information will be provided |
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## Training and evaluation data |
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More information will be provided |
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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: 10 |
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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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| 1.6002 | 0.97 | 14 | 1.4558 | 0.5024 | |
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| 1.4093 | 2.0 | 29 | 1.2320 | 0.5024 | |
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| 1.1068 | 2.97 | 43 | 1.0740 | 0.5951 | |
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| 0.9988 | 4.0 | 58 | 0.9967 | 0.6049 | |
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| 0.9099 | 4.97 | 72 | 0.9248 | 0.6 | |
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| 0.8674 | 6.0 | 87 | 0.8766 | 0.6780 | |
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| 0.8638 | 6.97 | 101 | 0.8656 | 0.6732 | |
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| 0.833 | 8.0 | 116 | 0.8395 | 0.6732 | |
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| 0.8211 | 8.97 | 130 | 0.8204 | 0.6927 | |
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| 0.8236 | 9.66 | 140 | 0.8204 | 0.6780 | |
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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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