transformers tensorboard safetensors swinv2 image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swinv2-tiny-patch4-window8-256 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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
---

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# 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
