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# mobilevitv2-1.0-imagenet1k-256-finetuned-swin-tiny

This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3595
- Accuracy: 0.5468

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6149        | 0.96  | 20   | 3.6094          | 0.0363   |
| 3.601         | 1.98  | 41   | 3.5936          | 0.0544   |
| 3.5892        | 2.99  | 62   | 3.5643          | 0.1057   |
| 3.5556        | 4.0   | 83   | 3.5195          | 0.1752   |
| 3.505         | 4.96  | 103  | 3.4422          | 0.2870   |
| 3.4072        | 5.98  | 124  | 3.2947          | 0.3172   |
| 3.2477        | 6.99  | 145  | 3.0629          | 0.3233   |
| 3.0508        | 8.0   | 166  | 2.8124          | 0.3444   |
| 2.8381        | 8.96  | 186  | 2.6019          | 0.3867   |
| 2.6407        | 9.98  | 207  | 2.4012          | 0.4018   |
| 2.5312        | 10.99 | 228  | 2.2300          | 0.4441   |
| 2.3687        | 12.0  | 249  | 2.0957          | 0.4411   |
| 2.2963        | 12.96 | 269  | 1.9972          | 0.4653   |
| 2.1898        | 13.98 | 290  | 1.9019          | 0.4743   |
| 2.0632        | 14.99 | 311  | 1.8381          | 0.4834   |
| 2.0279        | 16.0  | 332  | 1.7724          | 0.4955   |
| 1.998         | 16.96 | 352  | 1.7243          | 0.5015   |
| 1.9156        | 17.98 | 373  | 1.6919          | 0.5015   |
| 1.8914        | 18.99 | 394  | 1.6483          | 0.4985   |
| 1.8466        | 20.0  | 415  | 1.6211          | 0.5045   |
| 1.853         | 20.96 | 435  | 1.5899          | 0.5166   |
| 1.8124        | 21.98 | 456  | 1.5613          | 0.5015   |
| 1.7247        | 22.99 | 477  | 1.5355          | 0.5227   |
| 1.7034        | 24.0  | 498  | 1.5121          | 0.5287   |
| 1.6678        | 24.96 | 518  | 1.5000          | 0.5317   |
| 1.6832        | 25.98 | 539  | 1.4876          | 0.5287   |
| 1.6727        | 26.99 | 560  | 1.4796          | 0.5287   |
| 1.5744        | 28.0  | 581  | 1.4712          | 0.5227   |
| 1.5842        | 28.96 | 601  | 1.4492          | 0.5166   |
| 1.5416        | 29.98 | 622  | 1.4345          | 0.5347   |
| 1.5757        | 30.99 | 643  | 1.4229          | 0.5257   |
| 1.5574        | 32.0  | 664  | 1.4138          | 0.5378   |
| 1.5665        | 32.96 | 684  | 1.4077          | 0.5438   |
| 1.4837        | 33.98 | 705  | 1.3861          | 0.5438   |
| 1.5114        | 34.99 | 726  | 1.3956          | 0.5529   |
| 1.5207        | 36.0  | 747  | 1.3883          | 0.5468   |
| 1.4879        | 36.96 | 767  | 1.3750          | 0.5378   |
| 1.4547        | 37.98 | 788  | 1.3817          | 0.5408   |
| 1.4668        | 38.99 | 809  | 1.3643          | 0.5529   |
| 1.457         | 40.0  | 830  | 1.3669          | 0.5408   |
| 1.4604        | 40.96 | 850  | 1.3653          | 0.5498   |
| 1.4556        | 41.98 | 871  | 1.3621          | 0.5438   |
| 1.4852        | 42.99 | 892  | 1.3549          | 0.5498   |
| 1.4198        | 44.0  | 913  | 1.3461          | 0.5498   |
| 1.3824        | 44.96 | 933  | 1.3495          | 0.5498   |
| 1.4035        | 45.98 | 954  | 1.3495          | 0.5589   |
| 1.4586        | 46.99 | 975  | 1.3476          | 0.5529   |
| 1.4265        | 48.0  | 996  | 1.3481          | 0.5498   |
| 1.4563        | 48.19 | 1000 | 1.3595          | 0.5468   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1

attic_door bi-fold_door bi-fold_door_double bi-fold_glass_door bi-fold_glass_door_double casement_diamond_window casement_round_window casement_window concern door_opening door_opening_arch double_hung_window elevator_door exterior_door exterior_door_double exterior_glass_door exterior_glass_door_double fixed_arch_window fixed_diamond_window fixed_round_window fixed_sloping_left_window fixed_sloping_right_window fixed_windows french_door french_glass_door garage_door garage_door_double interior_door interior_glass_door interior_glass_door_double patio_door pocket_door pocket_door_double sauna_door sauna_glass_door sliding_door_double sliding_window