---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SW2-TO-DA
  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: 0.9193548387096774
---

<!-- 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. -->

# SW2-TO-DA

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: 0.2207
- Accuracy: 0.9194

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4955        | 0.97  | 14   | 1.5580          | 0.0806   |
| 1.3943        | 2.0   | 29   | 1.1316          | 0.6452   |
| 1.0056        | 2.97  | 43   | 0.6407          | 0.7419   |
| 0.7744        | 4.0   | 58   | 0.4265          | 0.8710   |
| 0.6022        | 4.97  | 72   | 0.4361          | 0.8548   |
| 0.5854        | 6.0   | 87   | 0.5508          | 0.8065   |
| 0.4581        | 6.97  | 101  | 0.3124          | 0.8548   |
| 0.386         | 8.0   | 116  | 0.3169          | 0.8548   |
| 0.347         | 8.97  | 130  | 0.2207          | 0.9194   |
| 0.3873        | 10.0  | 145  | 0.5969          | 0.8226   |
| 0.3508        | 10.97 | 159  | 0.3425          | 0.8871   |
| 0.274         | 12.0  | 174  | 0.3376          | 0.8710   |
| 0.2615        | 12.97 | 188  | 0.4913          | 0.8710   |
| 0.3118        | 14.0  | 203  | 0.4034          | 0.8871   |
| 0.2205        | 14.97 | 217  | 0.3167          | 0.8710   |
| 0.2325        | 16.0  | 232  | 0.3043          | 0.8871   |
| 0.1914        | 16.97 | 246  | 0.4256          | 0.8226   |
| 0.1997        | 18.0  | 261  | 0.3769          | 0.8548   |
| 0.1752        | 18.97 | 275  | 0.5875          | 0.8548   |
| 0.1685        | 20.0  | 290  | 0.4104          | 0.8871   |
| 0.1736        | 20.97 | 304  | 0.5481          | 0.8548   |
| 0.1901        | 22.0  | 319  | 0.3800          | 0.9032   |
| 0.1426        | 22.97 | 333  | 0.4425          | 0.8871   |
| 0.1251        | 24.0  | 348  | 0.3374          | 0.9032   |
| 0.1326        | 24.97 | 362  | 0.3627          | 0.8871   |
| 0.1271        | 26.0  | 377  | 0.4768          | 0.8710   |
| 0.1835        | 26.97 | 391  | 0.5604          | 0.8710   |
| 0.1378        | 28.0  | 406  | 0.4131          | 0.8871   |
| 0.1349        | 28.97 | 420  | 0.5103          | 0.8548   |
| 0.0999        | 30.0  | 435  | 0.3723          | 0.9194   |
| 0.1198        | 30.97 | 449  | 0.5361          | 0.8710   |
| 0.1195        | 32.0  | 464  | 0.4194          | 0.8871   |
| 0.0766        | 32.97 | 478  | 0.4133          | 0.8871   |
| 0.0862        | 34.0  | 493  | 0.4239          | 0.9032   |
| 0.1048        | 34.97 | 507  | 0.4120          | 0.9194   |
| 0.0902        | 36.0  | 522  | 0.4408          | 0.9032   |
| 0.088         | 36.97 | 536  | 0.4436          | 0.9032   |
| 0.089         | 38.0  | 551  | 0.4648          | 0.9032   |
| 0.1089        | 38.62 | 560  | 0.4650          | 0.8871   |


### Framework versions

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