---
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV24
  results: []
---

<!-- 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-humeda-DAV24

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8851
- Accuracy: 0.7059

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 6.5614        | 1.0     | 17   | 1.6096          | 0.3176   |
| 6.1279        | 2.0     | 34   | 1.5651          | 0.3176   |
| 5.5089        | 3.0     | 51   | 1.3188          | 0.5529   |
| 4.453         | 4.0     | 68   | 1.0195          | 0.6353   |
| 3.3808        | 5.0     | 85   | 0.9741          | 0.5882   |
| 2.7707        | 6.0     | 102  | 0.8365          | 0.6353   |
| 2.3091        | 7.0     | 119  | 0.7725          | 0.6588   |
| 1.9831        | 8.0     | 136  | 0.8312          | 0.6588   |
| 1.8284        | 9.0     | 153  | 0.8473          | 0.7059   |
| 1.511         | 10.0    | 170  | 0.7539          | 0.7176   |
| 1.2827        | 11.0    | 187  | 0.8067          | 0.7176   |
| 1.2072        | 12.0    | 204  | 0.7927          | 0.7176   |
| 1.2069        | 13.0    | 221  | 0.8184          | 0.6824   |
| 0.9242        | 14.0    | 238  | 0.8548          | 0.7059   |
| 0.9772        | 15.0    | 255  | 0.8374          | 0.7294   |
| 0.8412        | 16.0    | 272  | 0.8340          | 0.7176   |
| 0.8921        | 17.0    | 289  | 0.8729          | 0.6941   |
| 0.7975        | 18.0    | 306  | 0.9115          | 0.7059   |
| 0.8107        | 19.0    | 323  | 0.8830          | 0.6941   |
| 0.7131        | 20.0    | 340  | 0.9049          | 0.6941   |
| 0.6777        | 21.0    | 357  | 0.8895          | 0.7059   |
| 0.6557        | 22.0    | 374  | 0.8831          | 0.7059   |
| 0.6555        | 23.0    | 391  | 0.8846          | 0.7059   |
| 0.7766        | 23.5455 | 400  | 0.8851          | 0.7059   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0