---
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: 0.616822429906542
---

<!-- 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: 0.6580
- Accuracy: 0.6168

## 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        | 1.0   | 8    | 4.5659          | 0.4112   |
| 4.5175        | 2.0   | 16   | 3.6471          | 0.4112   |
| 3.927         | 3.0   | 24   | 1.6286          | 0.4112   |
| 1.6081        | 4.0   | 32   | 0.6781          | 0.5888   |
| 0.7702        | 5.0   | 40   | 0.8357          | 0.5888   |
| 0.7702        | 6.0   | 48   | 0.6766          | 0.5888   |
| 0.7502        | 7.0   | 56   | 0.7522          | 0.4112   |
| 0.7266        | 8.0   | 64   | 0.6792          | 0.5888   |
| 0.6954        | 9.0   | 72   | 0.6881          | 0.5888   |
| 0.6808        | 10.0  | 80   | 0.6780          | 0.5888   |
| 0.6808        | 11.0  | 88   | 0.7130          | 0.5888   |
| 0.7068        | 12.0  | 96   | 0.6771          | 0.5888   |
| 0.6792        | 13.0  | 104  | 0.6779          | 0.5888   |
| 0.6841        | 14.0  | 112  | 0.6766          | 0.5888   |
| 0.6777        | 15.0  | 120  | 0.6861          | 0.5888   |
| 0.6777        | 16.0  | 128  | 0.6773          | 0.5888   |
| 0.6818        | 17.0  | 136  | 0.6806          | 0.5888   |
| 0.6747        | 18.0  | 144  | 0.6929          | 0.5888   |
| 0.6814        | 19.0  | 152  | 0.6767          | 0.5888   |
| 0.6714        | 20.0  | 160  | 0.6745          | 0.5888   |
| 0.6714        | 21.0  | 168  | 0.6852          | 0.5888   |
| 0.6765        | 22.0  | 176  | 0.6816          | 0.5514   |
| 0.6822        | 23.0  | 184  | 0.6983          | 0.5888   |
| 0.6816        | 24.0  | 192  | 0.6706          | 0.5888   |
| 0.6868        | 25.0  | 200  | 0.6982          | 0.5701   |
| 0.6868        | 26.0  | 208  | 0.6878          | 0.5701   |
| 0.6724        | 27.0  | 216  | 0.6785          | 0.5888   |
| 0.6613        | 28.0  | 224  | 0.6843          | 0.5888   |
| 0.6501        | 29.0  | 232  | 0.7126          | 0.5888   |
| 0.6566        | 30.0  | 240  | 0.6917          | 0.5701   |
| 0.6566        | 31.0  | 248  | 0.7020          | 0.5607   |
| 0.6583        | 32.0  | 256  | 0.6782          | 0.5888   |
| 0.6501        | 33.0  | 264  | 0.6647          | 0.5888   |
| 0.654         | 34.0  | 272  | 0.6603          | 0.5981   |
| 0.6604        | 35.0  | 280  | 0.6873          | 0.5794   |
| 0.6604        | 36.0  | 288  | 0.6591          | 0.5794   |
| 0.6456        | 37.0  | 296  | 0.6580          | 0.6168   |
| 0.6483        | 38.0  | 304  | 0.6702          | 0.5981   |
| 0.6151        | 39.0  | 312  | 0.6785          | 0.5981   |
| 0.6291        | 40.0  | 320  | 0.6806          | 0.5981   |


### Framework versions

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