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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-RH
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: 0.6822429906542056
---
<!-- 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-RH
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: 0.6396
- Accuracy: 0.6822
## 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: 4e-05
- 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 | 4.6265 | 0.4112 |
| 4.5369 | 2.0 | 16 | 4.5295 | 0.4112 |
| 4.6305 | 3.0 | 24 | 4.1439 | 0.4112 |
| 4.0918 | 4.0 | 32 | 3.3693 | 0.4112 |
| 3.1767 | 5.0 | 40 | 2.4325 | 0.4112 |
| 3.1767 | 6.0 | 48 | 1.5422 | 0.4112 |
| 2.0113 | 7.0 | 56 | 0.8834 | 0.4112 |
| 1.0593 | 8.0 | 64 | 0.6790 | 0.5888 |
| 0.696 | 9.0 | 72 | 0.7044 | 0.5888 |
| 0.6893 | 10.0 | 80 | 0.6778 | 0.5888 |
| 0.6893 | 11.0 | 88 | 0.6866 | 0.5888 |
| 0.6961 | 12.0 | 96 | 0.6934 | 0.5888 |
| 0.7329 | 13.0 | 104 | 0.6915 | 0.5888 |
| 0.6948 | 14.0 | 112 | 0.6762 | 0.5888 |
| 0.6771 | 15.0 | 120 | 0.6795 | 0.5888 |
| 0.6771 | 16.0 | 128 | 0.6801 | 0.5888 |
| 0.6763 | 17.0 | 136 | 0.6820 | 0.5888 |
| 0.6822 | 18.0 | 144 | 0.6800 | 0.5888 |
| 0.6723 | 19.0 | 152 | 0.6741 | 0.5888 |
| 0.6757 | 20.0 | 160 | 0.6815 | 0.5888 |
| 0.6757 | 21.0 | 168 | 0.6729 | 0.5888 |
| 0.6711 | 22.0 | 176 | 0.6812 | 0.5888 |
| 0.6784 | 23.0 | 184 | 0.6781 | 0.5794 |
| 0.6665 | 24.0 | 192 | 0.6698 | 0.5794 |
| 0.6723 | 25.0 | 200 | 0.6647 | 0.5981 |
| 0.6723 | 26.0 | 208 | 0.6762 | 0.5794 |
| 0.6675 | 27.0 | 216 | 0.6597 | 0.5701 |
| 0.6628 | 28.0 | 224 | 0.6563 | 0.6355 |
| 0.6478 | 29.0 | 232 | 0.6791 | 0.5794 |
| 0.6642 | 30.0 | 240 | 0.6574 | 0.5888 |
| 0.6642 | 31.0 | 248 | 0.6556 | 0.5607 |
| 0.654 | 32.0 | 256 | 0.6523 | 0.5888 |
| 0.6602 | 33.0 | 264 | 0.6464 | 0.6262 |
| 0.6535 | 34.0 | 272 | 0.6450 | 0.6168 |
| 0.6506 | 35.0 | 280 | 0.6550 | 0.5794 |
| 0.6506 | 36.0 | 288 | 0.6438 | 0.6075 |
| 0.6533 | 37.0 | 296 | 0.6396 | 0.6822 |
| 0.6443 | 38.0 | 304 | 0.6383 | 0.6636 |
| 0.6263 | 39.0 | 312 | 0.6378 | 0.6449 |
| 0.6283 | 40.0 | 320 | 0.6379 | 0.6449 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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