---
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-RD-aptos19
  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: 0.6739130434782609
---

<!-- 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-RD-aptos19

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: 144573075075950992480149202324684800.0000
- Accuracy: 0.6739

## 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: 0.00015
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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                                    | 0.86  | 3    | 144573075075950992480149202324684800.0000 | 0.4565   |
| No log                                    | 2.0   | 7    | 144573075075950992480149202324684800.0000 | 0.4565   |
| 141735823463928302525633790371430400.0000 | 2.86  | 10   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 141735823463928302525633790371430400.0000 | 4.0   | 14   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 141735823463928302525633790371430400.0000 | 4.86  | 17   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 148386187888478135085935683952443392.0000 | 6.0   | 21   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 148386187888478135085935683952443392.0000 | 6.86  | 24   | 144573075075950992480149202324684800.0000 | 0.4783   |
| 148386187888478135085935683952443392.0000 | 8.0   | 28   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 166674646480500797315403436963921920.0000 | 8.86  | 31   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 166674646480500797315403436963921920.0000 | 10.0  | 35   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 166674646480500797315403436963921920.0000 | 10.86 | 38   | 144573075075950992480149202324684800.0000 | 0.4565   |
| 123031678471642034838718731348082688.0000 | 12.0  | 42   | 144573075075950992480149202324684800.0000 | 0.5217   |
| 123031678471642034838718731348082688.0000 | 12.86 | 45   | 144573075075950992480149202324684800.0000 | 0.6087   |
| 123031678471642034838718731348082688.0000 | 14.0  | 49   | 144573075075950992480149202324684800.0000 | 0.5435   |
| 160439944687765812243898756589682688.0000 | 14.86 | 52   | 144573075075950992480149202324684800.0000 | 0.6522   |
| 160439944687765812243898756589682688.0000 | 16.0  | 56   | 144573075075950992480149202324684800.0000 | 0.5870   |
| 160439944687765812243898756589682688.0000 | 16.86 | 59   | 144573075075950992480149202324684800.0000 | 0.5652   |
| 151295747083019479456202288017702912.0000 | 18.0  | 63   | 144573075075950992480149202324684800.0000 | 0.6087   |
| 151295747083019479456202288017702912.0000 | 18.86 | 66   | 144573075075950992480149202324684800.0000 | 0.6304   |
| 142151454404478133240649521934893056.0000 | 20.0  | 70   | 144573075075950992480149202324684800.0000 | 0.6522   |
| 142151454404478133240649521934893056.0000 | 20.86 | 73   | 144573075075950992480149202324684800.0000 | 0.6739   |
| 142151454404478133240649521934893056.0000 | 22.0  | 77   | 144573075075950992480149202324684800.0000 | 0.6739   |
| 137163724661555136131785556085440512.0000 | 22.86 | 80   | 144573075075950992480149202324684800.0000 | 0.6304   |
| 137163724661555136131785556085440512.0000 | 24.0  | 84   | 144573075075950992480149202324684800.0000 | 0.6304   |
| 137163724661555136131785556085440512.0000 | 24.86 | 87   | 144573075075950992480149202324684800.0000 | 0.6739   |
| 137163692970290119358004074442129408.0000 | 26.0  | 91   | 144573075075950992480149202324684800.0000 | 0.6304   |
| 137163692970290119358004074442129408.0000 | 26.86 | 94   | 144573075075950992480149202324684800.0000 | 0.6522   |
| 137163692970290119358004074442129408.0000 | 28.0  | 98   | 144573075075950992480149202324684800.0000 | 0.6522   |
| 155452183253577798361253309096919040.0000 | 28.86 | 101  | 144573075075950992480149202324684800.0000 | 0.6739   |
| 155452183253577798361253309096919040.0000 | 30.0  | 105  | 144573075075950992480149202324684800.0000 | 0.6522   |
| 155452183253577798361253309096919040.0000 | 30.86 | 108  | 144573075075950992480149202324684800.0000 | 0.6522   |
| 139657557841751617912436057366855680.0000 | 32.0  | 112  | 144573075075950992480149202324684800.0000 | 0.6522   |
| 139657557841751617912436057366855680.0000 | 32.86 | 115  | 144573075075950992480149202324684800.0000 | 0.6522   |
| 139657557841751617912436057366855680.0000 | 34.0  | 119  | 144573075075950992480149202324684800.0000 | 0.6304   |
| 141735791772663285751852308728119296.0000 | 34.29 | 120  | 144573075075950992480149202324684800.0000 | 0.6304   |


### Framework versions

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