---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2
  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. -->

# swin-tiny-patch4-window7-224-finetuned_ASL_Isolated_Swin_dataset2

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the ASL_Isolated_Swin_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0439
- Accuracy: 0.9846

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5603        | 1.09  | 100  | 1.0931          | 0.6423   |
| 0.9055        | 2.17  | 200  | 0.5069          | 0.8615   |
| 0.4254        | 3.26  | 300  | 0.5634          | 0.8154   |
| 0.5814        | 4.35  | 400  | 0.2883          | 0.9154   |
| 0.4953        | 5.43  | 500  | 0.2710          | 0.9154   |
| 0.4456        | 6.52  | 600  | 0.2451          | 0.9346   |
| 0.4524        | 7.61  | 700  | 0.2625          | 0.9308   |
| 0.3095        | 8.7   | 800  | 0.2397          | 0.9462   |
| 0.3224        | 9.78  | 900  | 0.1787          | 0.9385   |
| 0.4069        | 10.87 | 1000 | 0.3376          | 0.9231   |
| 0.3467        | 11.96 | 1100 | 0.1603          | 0.9538   |
| 0.469         | 13.04 | 1200 | 0.2247          | 0.9423   |
| 0.4523        | 14.13 | 1300 | 0.1552          | 0.9538   |
| 0.2923        | 15.22 | 1400 | 0.3376          | 0.9346   |
| 0.3139        | 16.3  | 1500 | 0.1449          | 0.9577   |
| 0.3873        | 17.39 | 1600 | 0.1495          | 0.9654   |
| 0.2994        | 18.48 | 1700 | 0.1821          | 0.9654   |
| 0.2611        | 19.57 | 1800 | 0.1294          | 0.9769   |
| 0.1883        | 20.65 | 1900 | 0.0879          | 0.9731   |
| 0.2076        | 21.74 | 2000 | 0.1969          | 0.95     |
| 0.3531        | 22.83 | 2100 | 0.2135          | 0.9538   |
| 0.4339        | 23.91 | 2200 | 0.1030          | 0.9615   |
| 0.2959        | 25.0  | 2300 | 0.1579          | 0.9731   |
| 0.1546        | 26.09 | 2400 | 0.1648          | 0.9692   |
| 0.1315        | 27.17 | 2500 | 0.1514          | 0.9577   |
| 0.2191        | 28.26 | 2600 | 0.1257          | 0.9538   |
| 0.16          | 29.35 | 2700 | 0.1162          | 0.9692   |
| 0.1567        | 30.43 | 2800 | 0.1252          | 0.9731   |
| 0.1147        | 31.52 | 2900 | 0.2642          | 0.9577   |
| 0.1434        | 32.61 | 3000 | 0.1371          | 0.9769   |
| 0.2488        | 33.7  | 3100 | 0.1161          | 0.9769   |
| 0.1646        | 34.78 | 3200 | 0.2052          | 0.9615   |
| 0.1326        | 35.87 | 3300 | 0.1995          | 0.9769   |
| 0.137         | 36.96 | 3400 | 0.1124          | 0.9731   |
| 0.1633        | 38.04 | 3500 | 0.1620          | 0.9692   |
| 0.1593        | 39.13 | 3600 | 0.1838          | 0.9731   |
| 0.2192        | 40.22 | 3700 | 0.1331          | 0.9769   |
| 0.1495        | 41.3  | 3800 | 0.1291          | 0.9731   |
| 0.226         | 42.39 | 3900 | 0.1090          | 0.9692   |
| 0.1383        | 43.48 | 4000 | 0.0994          | 0.9654   |
| 0.0491        | 44.57 | 4100 | 0.0660          | 0.9769   |
| 0.1034        | 45.65 | 4200 | 0.0698          | 0.9808   |
| 0.0893        | 46.74 | 4300 | 0.0439          | 0.9846   |
| 0.1789        | 47.83 | 4400 | 0.0577          | 0.9808   |
| 0.0569        | 48.91 | 4500 | 0.0547          | 0.9846   |
| 0.1113        | 50.0  | 4600 | 0.0605          | 0.9846   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1