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---
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mobilevitv2-1.0-imagenet1k-256-finetuned-swin-tiny
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. -->
# 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
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