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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: sashes_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.875968992248062
---
<!-- 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. -->
# sashes_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3784
- Accuracy: 0.8760
## 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: 5e-05
- 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: 112
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------:|
| No log | 0.9697 | 8 | 2.2973 | 0.1434 |
| 2.2994 | 1.9394 | 16 | 2.2717 | 0.1957 |
| 2.2791 | 2.9091 | 24 | 2.2377 | 0.2287 |
| 2.2378 | 4.0 | 33 | 2.1866 | 0.3178 |
| 2.1604 | 4.9697 | 41 | 2.1096 | 0.3934 |
| 2.1604 | 5.9394 | 49 | 2.0257 | 0.4322 |
| 2.0801 | 6.9091 | 57 | 1.9312 | 0.4264 |
| 1.9587 | 8.0 | 66 | 1.7939 | 0.4942 |
| 1.821 | 8.9697 | 74 | 1.6869 | 0.5465 |
| 1.6903 | 9.9394 | 82 | 1.6025 | 0.5736 |
| 1.5687 | 10.9091 | 90 | 1.4849 | 0.6202 |
| 1.5687 | 12.0 | 99 | 1.4674 | 0.5407 |
| 1.4183 | 12.9697 | 107 | 1.3539 | 0.6163 |
| 1.3907 | 13.9394 | 115 | 1.2365 | 0.6938 |
| 1.3058 | 14.9091 | 123 | 1.2258 | 0.6938 |
| 1.2181 | 16.0 | 132 | 1.1759 | 0.6822 |
| 1.1537 | 16.9697 | 140 | 1.1413 | 0.7074 |
| 1.1537 | 17.9394 | 148 | 1.0586 | 0.7248 |
| 1.0819 | 18.9091 | 156 | 1.0059 | 0.7558 |
| 0.9905 | 20.0 | 165 | 0.9575 | 0.7578 |
| 1.0055 | 20.9697 | 173 | 0.9807 | 0.7442 |
| 0.9484 | 21.9394 | 181 | 0.9553 | 0.7539 |
| 0.9484 | 22.9091 | 189 | 0.8213 | 0.8004 |
| 0.8974 | 24.0 | 198 | 0.8305 | 0.8043 |
| 0.8545 | 24.9697 | 206 | 0.8273 | 0.7849 |
| 0.8724 | 25.9394 | 214 | 0.8177 | 0.7519 |
| 0.8642 | 26.9091 | 222 | 0.7692 | 0.7926 |
| 0.7609 | 28.0 | 231 | 0.7293 | 0.8062 |
| 0.7609 | 28.9697 | 239 | 0.7001 | 0.8198 |
| 0.7418 | 29.9394 | 247 | 0.7899 | 0.7636 |
| 0.7552 | 30.9091 | 255 | 0.6595 | 0.8101 |
| 0.7291 | 32.0 | 264 | 0.6971 | 0.7907 |
| 0.693 | 32.9697 | 272 | 0.7215 | 0.7946 |
| 0.6891 | 33.9394 | 280 | 0.6980 | 0.8004 |
| 0.6891 | 34.9091 | 288 | 0.6200 | 0.8372 |
| 0.6936 | 36.0 | 297 | 0.7245 | 0.7733 |
| 0.6698 | 36.9697 | 305 | 0.6724 | 0.7984 |
| 0.6502 | 37.9394 | 313 | 0.6701 | 0.8023 |
| 0.6988 | 38.9091 | 321 | 0.6049 | 0.8236 |
| 0.6709 | 40.0 | 330 | 0.6397 | 0.7965 |
| 0.6709 | 40.9697 | 338 | 0.5654 | 0.8391 |
| 0.652 | 41.9394 | 346 | 0.6371 | 0.8101 |
| 0.64 | 42.9091 | 354 | 0.6341 | 0.8062 |
| 0.6368 | 44.0 | 363 | 0.5662 | 0.8527 |
| 0.595 | 44.9697 | 371 | 0.5744 | 0.8411 |
| 0.595 | 45.9394 | 379 | 0.5465 | 0.8430 |
| 0.5823 | 46.9091 | 387 | 0.6254 | 0.7984 |
| 0.5514 | 48.0 | 396 | 0.5368 | 0.8333 |
| 0.5693 | 48.9697 | 404 | 0.5705 | 0.8043 |
| 0.5244 | 49.9394 | 412 | 0.5685 | 0.8314 |
| 0.5495 | 50.9091 | 420 | 0.5811 | 0.8120 |
| 0.5495 | 52.0 | 429 | 0.5037 | 0.8469 |
| 0.5501 | 52.9697 | 437 | 0.5423 | 0.8372 |
| 0.5405 | 53.9394 | 445 | 0.5487 | 0.8178 |
| 0.534 | 54.9091 | 453 | 0.5607 | 0.8217 |
| 0.5502 | 56.0 | 462 | 0.5141 | 0.8198 |
| 0.4772 | 56.9697 | 470 | 0.4813 | 0.8605 |
| 0.4772 | 57.9394 | 478 | 0.5007 | 0.8566 |
| 0.4823 | 58.9091 | 486 | 0.4847 | 0.8624 |
| 0.5107 | 60.0 | 495 | 0.5273 | 0.8333 |
| 0.5205 | 60.9697 | 503 | 0.4981 | 0.8430 |
| 0.5171 | 61.9394 | 511 | 0.4819 | 0.8430 |
| 0.5171 | 62.9091 | 519 | 0.4415 | 0.8682 |
| 0.5498 | 64.0 | 528 | 0.4578 | 0.8566 |
| 0.4732 | 64.9697 | 536 | 0.4614 | 0.8450 |
| 0.4623 | 65.9394 | 544 | 0.4923 | 0.8488 |
| 0.4406 | 66.9091 | 552 | 0.4556 | 0.8547 |
| 0.4889 | 68.0 | 561 | 0.4727 | 0.8488 |
| 0.4889 | 68.9697 | 569 | 0.4746 | 0.8469 |
| 0.4532 | 69.9394 | 577 | 0.4496 | 0.8585 |
| 0.3988 | 70.9091 | 585 | 0.4260 | 0.8702 |
| 0.4608 | 72.0 | 594 | 0.4464 | 0.8547 |
| 0.4429 | 72.9697 | 602 | 0.3946 | 0.8818 |
| 0.4502 | 73.9394 | 610 | 0.4566 | 0.8527 |
| 0.4502 | 74.9091 | 618 | 0.4472 | 0.8663 |
| 0.4381 | 76.0 | 627 | 0.4701 | 0.8372 |
| 0.4437 | 76.9697 | 635 | 0.4351 | 0.8488 |
| 0.4223 | 77.9394 | 643 | 0.4011 | 0.8779 |
| 0.4121 | 78.9091 | 651 | 0.4328 | 0.8547 |
| 0.4164 | 80.0 | 660 | 0.3908 | 0.8857 |
| 0.4164 | 80.9697 | 668 | 0.3774 | 0.8876 |
| 0.418 | 81.9394 | 676 | 0.4397 | 0.8643 |
| 0.3961 | 82.9091 | 684 | 0.4500 | 0.8585 |
| 0.4035 | 84.0 | 693 | 0.3968 | 0.8624 |
| 0.4269 | 84.9697 | 701 | 0.4457 | 0.8566 |
| 0.4269 | 85.9394 | 709 | 0.3987 | 0.8740 |
| 0.3694 | 86.9091 | 717 | 0.4074 | 0.8760 |
| 0.3642 | 88.0 | 726 | 0.3781 | 0.9012 |
| 0.3985 | 88.9697 | 734 | 0.3575 | 0.8934 |
| 0.4237 | 89.9394 | 742 | 0.4313 | 0.8508 |
| 0.4156 | 90.9091 | 750 | 0.3504 | 0.8934 |
| 0.4156 | 92.0 | 759 | 0.4116 | 0.8566 |
| 0.389 | 92.9697 | 767 | 0.3739 | 0.8779 |
| 0.3934 | 93.9394 | 775 | 0.3990 | 0.8779 |
| 0.4231 | 94.9091 | 783 | 0.4164 | 0.8624 |
| 0.3792 | 96.0 | 792 | 0.3808 | 0.8721 |
| 0.3928 | 96.9697 | 800 | 0.3534 | 0.8915 |
| 0.3928 | 97.9394 | 808 | 0.3643 | 0.8798 |
| 0.4003 | 98.9091 | 816 | 0.4150 | 0.8624 |
| 0.3929 | 100.0 | 825 | 0.3477 | 0.9050 |
| 0.3992 | 100.9697 | 833 | 0.4037 | 0.8682 |
| 0.387 | 101.9394 | 841 | 0.3453 | 0.9050 |
| 0.387 | 102.9091 | 849 | 0.4012 | 0.8682 |
| 0.3942 | 104.0 | 858 | 0.3843 | 0.8915 |
| 0.3794 | 104.9697 | 866 | 0.3478 | 0.8798 |
| 0.3794 | 105.9394 | 874 | 0.3111 | 0.9167 |
| 0.396 | 106.9091 | 882 | 0.3588 | 0.8818 |
| 0.3767 | 108.0 | 891 | 0.3602 | 0.8837 |
| 0.3767 | 108.6061 | 896 | 0.3784 | 0.8760 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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