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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-DMAE-8e-6
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.10869565217391304
---
<!-- 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-DMAE-8e-6
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: 6.3664
- Accuracy: 0.1087
## 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: 8e-06
- 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 | 7.9427 | 0.1087 |
| No log | 2.0 | 7 | 7.9381 | 0.1087 |
| 7.9636 | 2.86 | 10 | 7.9301 | 0.1087 |
| 7.9636 | 4.0 | 14 | 7.9088 | 0.1087 |
| 7.9636 | 4.86 | 17 | 7.8857 | 0.1087 |
| 7.8732 | 6.0 | 21 | 7.8450 | 0.1087 |
| 7.8732 | 6.86 | 24 | 7.8049 | 0.1087 |
| 7.8732 | 8.0 | 28 | 7.7376 | 0.1087 |
| 7.6568 | 8.86 | 31 | 7.6783 | 0.1087 |
| 7.6568 | 10.0 | 35 | 7.5943 | 0.1087 |
| 7.6568 | 10.86 | 38 | 7.5288 | 0.1087 |
| 7.7458 | 12.0 | 42 | 7.4353 | 0.1087 |
| 7.7458 | 12.86 | 45 | 7.3610 | 0.1087 |
| 7.7458 | 14.0 | 49 | 7.2614 | 0.1087 |
| 7.3025 | 14.86 | 52 | 7.1894 | 0.1087 |
| 7.3025 | 16.0 | 56 | 7.0993 | 0.1087 |
| 7.3025 | 16.86 | 59 | 7.0348 | 0.1087 |
| 7.0862 | 18.0 | 63 | 6.9525 | 0.1087 |
| 7.0862 | 18.86 | 66 | 6.8945 | 0.1087 |
| 6.9553 | 20.0 | 70 | 6.8253 | 0.1087 |
| 6.9553 | 20.86 | 73 | 6.7795 | 0.1087 |
| 6.9553 | 22.0 | 77 | 6.7202 | 0.1087 |
| 6.8024 | 22.86 | 80 | 6.6757 | 0.1087 |
| 6.8024 | 24.0 | 84 | 6.6210 | 0.1087 |
| 6.8024 | 24.86 | 87 | 6.5785 | 0.1087 |
| 6.6652 | 26.0 | 91 | 6.5275 | 0.1087 |
| 6.6652 | 26.86 | 94 | 6.4949 | 0.1087 |
| 6.6652 | 28.0 | 98 | 6.4589 | 0.1087 |
| 6.467 | 28.86 | 101 | 6.4354 | 0.1087 |
| 6.467 | 30.0 | 105 | 6.4094 | 0.1087 |
| 6.467 | 30.86 | 108 | 6.3946 | 0.1087 |
| 6.4984 | 32.0 | 112 | 6.3796 | 0.1087 |
| 6.4984 | 32.86 | 115 | 6.3719 | 0.1087 |
| 6.4984 | 34.0 | 119 | 6.3668 | 0.1087 |
| 6.4603 | 34.29 | 120 | 6.3664 | 0.1087 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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