---
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-Diabetic-Retinopathy
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8090909090909091
---

<!-- 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-Diabetic-Retinopathy

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: 0.5203
- Accuracy: 0.8091

## Model description

More information needed

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.6054          | 0.4909   |
| 1.6039        | 2.0   | 10   | 1.5774          | 0.4909   |
| 1.6039        | 3.0   | 15   | 1.4627          | 0.4909   |
| 1.4766        | 4.0   | 20   | 1.3211          | 0.4909   |
| 1.4766        | 5.0   | 25   | 1.2294          | 0.4909   |
| 1.2308        | 6.0   | 30   | 1.0657          | 0.4909   |
| 1.2308        | 7.0   | 35   | 0.9504          | 0.6545   |
| 1.017         | 8.0   | 40   | 0.8463          | 0.7364   |
| 1.017         | 9.0   | 45   | 0.7463          | 0.7455   |
| 0.8345        | 10.0  | 50   | 0.6948          | 0.7455   |
| 0.8345        | 11.0  | 55   | 0.6460          | 0.7545   |
| 0.7594        | 12.0  | 60   | 0.6403          | 0.7545   |
| 0.7594        | 13.0  | 65   | 0.6319          | 0.7545   |
| 0.7228        | 14.0  | 70   | 0.5999          | 0.7455   |
| 0.7228        | 15.0  | 75   | 0.5922          | 0.7545   |
| 0.6851        | 16.0  | 80   | 0.5955          | 0.7636   |
| 0.6851        | 17.0  | 85   | 0.5731          | 0.7545   |
| 0.6549        | 18.0  | 90   | 0.5603          | 0.7818   |
| 0.6549        | 19.0  | 95   | 0.5386          | 0.7818   |
| 0.643         | 20.0  | 100  | 0.5424          | 0.7727   |
| 0.643         | 21.0  | 105  | 0.5295          | 0.7909   |
| 0.5951        | 22.0  | 110  | 0.5203          | 0.8091   |
| 0.5951        | 23.0  | 115  | 0.5162          | 0.7909   |
| 0.5913        | 24.0  | 120  | 0.5095          | 0.7818   |
| 0.5913        | 25.0  | 125  | 0.5140          | 0.7909   |
| 0.5462        | 26.0  | 130  | 0.5167          | 0.7636   |
| 0.5462        | 27.0  | 135  | 0.4943          | 0.7909   |
| 0.5538        | 28.0  | 140  | 0.4844          | 0.7636   |
| 0.5538        | 29.0  | 145  | 0.4821          | 0.7727   |
| 0.5497        | 30.0  | 150  | 0.4952          | 0.7727   |
| 0.5497        | 31.0  | 155  | 0.4995          | 0.7818   |
| 0.4923        | 32.0  | 160  | 0.4910          | 0.7727   |
| 0.4923        | 33.0  | 165  | 0.5029          | 0.7818   |
| 0.5228        | 34.0  | 170  | 0.5083          | 0.7818   |
| 0.5228        | 35.0  | 175  | 0.4984          | 0.7909   |
| 0.4986        | 36.0  | 180  | 0.4914          | 0.7909   |
| 0.4986        | 37.0  | 185  | 0.4926          | 0.7909   |
| 0.5154        | 38.0  | 190  | 0.4915          | 0.8      |
| 0.5154        | 39.0  | 195  | 0.4886          | 0.8      |
| 0.5081        | 40.0  | 200  | 0.4875          | 0.8      |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0