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---
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
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