---
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: 0.8154362416107382
---

<!-- 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: 0.6445
- Accuracy: 0.8154

## Model description

Predict Expansion Grade - Gardner Score from an embryo image


## Intended uses & limitations

More information will be provided

## Training and evaluation data

More information will be provided

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6002        | 0.97  | 14   | 1.4558          | 0.5024   |
| 1.4093        | 2.0   | 29   | 1.2320          | 0.5024   |
| 1.1068        | 2.97  | 43   | 1.0740          | 0.5951   |
| 0.9988        | 4.0   | 58   | 0.9967          | 0.6049   |
| 0.9099        | 4.97  | 72   | 0.9248          | 0.6      |
| 0.8674        | 6.0   | 87   | 0.8766          | 0.6780   |
| 0.8638        | 6.97  | 101  | 0.8656          | 0.6732   |
| 0.833         | 8.0   | 116  | 0.8395          | 0.6732   |
| 0.8211        | 8.97  | 130  | 0.8204          | 0.6927   |
| 0.8236        | 9.66  | 140  | 0.8204          | 0.6780   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0