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---
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
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