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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-icm-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: 0.6428571428571429
---
<!-- 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-icm-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: 1.0741
- Accuracy: 0.6429
## Model description
Predict Inner Cell Mass 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0925 | 0.94 | 11 | 1.0631 | 0.7952 |
| 0.9552 | 1.96 | 23 | 0.6336 | 0.7952 |
| 0.6566 | 2.98 | 35 | 0.5356 | 0.7952 |
| 0.5686 | 4.0 | 47 | 0.5150 | 0.7952 |
| 0.5703 | 4.94 | 58 | 0.5129 | 0.7952 |
| 0.5726 | 5.96 | 70 | 0.5154 | 0.7952 |
| 0.5482 | 6.98 | 82 | 0.5142 | 0.7952 |
| 0.568 | 8.0 | 94 | 0.5109 | 0.7952 |
| 0.5245 | 8.94 | 105 | 0.5134 | 0.7952 |
| 0.5979 | 9.96 | 117 | 0.5238 | 0.7952 |
| 0.5442 | 10.98 | 129 | 0.5076 | 0.7952 |
| 0.545 | 12.0 | 141 | 0.5062 | 0.7952 |
| 0.5514 | 12.94 | 152 | 0.5013 | 0.7952 |
| 0.5377 | 13.96 | 164 | 0.5045 | 0.7952 |
| 0.5282 | 14.98 | 176 | 0.5038 | 0.7952 |
| 0.5389 | 16.0 | 188 | 0.4994 | 0.7952 |
| 0.5039 | 16.94 | 199 | 0.4996 | 0.7952 |
| 0.5348 | 17.96 | 211 | 0.4940 | 0.7952 |
| 0.5426 | 18.72 | 220 | 0.4947 | 0.7952 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
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