---
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