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---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_sgd_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8714524207011686
---
<!-- 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. -->
# smids_10x_beit_large_sgd_0001_fold1
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3289
- Accuracy: 0.8715
## 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
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9122 | 1.0 | 751 | 1.0277 | 0.4591 |
| 0.7509 | 2.0 | 1502 | 0.8635 | 0.6194 |
| 0.6317 | 3.0 | 2253 | 0.7448 | 0.7012 |
| 0.5452 | 4.0 | 3004 | 0.6612 | 0.7446 |
| 0.5954 | 5.0 | 3755 | 0.5972 | 0.7830 |
| 0.5075 | 6.0 | 4506 | 0.5495 | 0.7930 |
| 0.5045 | 7.0 | 5257 | 0.5158 | 0.8130 |
| 0.4666 | 8.0 | 6008 | 0.4891 | 0.8147 |
| 0.4159 | 9.0 | 6759 | 0.4686 | 0.8247 |
| 0.4231 | 10.0 | 7510 | 0.4504 | 0.8280 |
| 0.4497 | 11.0 | 8261 | 0.4359 | 0.8364 |
| 0.3539 | 12.0 | 9012 | 0.4229 | 0.8381 |
| 0.3554 | 13.0 | 9763 | 0.4122 | 0.8414 |
| 0.3441 | 14.0 | 10514 | 0.4038 | 0.8414 |
| 0.3331 | 15.0 | 11265 | 0.3962 | 0.8431 |
| 0.3376 | 16.0 | 12016 | 0.3885 | 0.8431 |
| 0.374 | 17.0 | 12767 | 0.3827 | 0.8431 |
| 0.3157 | 18.0 | 13518 | 0.3768 | 0.8464 |
| 0.3563 | 19.0 | 14269 | 0.3725 | 0.8514 |
| 0.3183 | 20.0 | 15020 | 0.3682 | 0.8548 |
| 0.2569 | 21.0 | 15771 | 0.3646 | 0.8598 |
| 0.312 | 22.0 | 16522 | 0.3608 | 0.8581 |
| 0.3262 | 23.0 | 17273 | 0.3576 | 0.8598 |
| 0.3722 | 24.0 | 18024 | 0.3550 | 0.8598 |
| 0.3339 | 25.0 | 18775 | 0.3524 | 0.8598 |
| 0.3725 | 26.0 | 19526 | 0.3497 | 0.8631 |
| 0.35 | 27.0 | 20277 | 0.3474 | 0.8664 |
| 0.3858 | 28.0 | 21028 | 0.3456 | 0.8648 |
| 0.3212 | 29.0 | 21779 | 0.3439 | 0.8664 |
| 0.3222 | 30.0 | 22530 | 0.3422 | 0.8681 |
| 0.2584 | 31.0 | 23281 | 0.3410 | 0.8664 |
| 0.3877 | 32.0 | 24032 | 0.3393 | 0.8698 |
| 0.3116 | 33.0 | 24783 | 0.3380 | 0.8698 |
| 0.3141 | 34.0 | 25534 | 0.3366 | 0.8715 |
| 0.3279 | 35.0 | 26285 | 0.3358 | 0.8681 |
| 0.2798 | 36.0 | 27036 | 0.3348 | 0.8715 |
| 0.3928 | 37.0 | 27787 | 0.3341 | 0.8715 |
| 0.3 | 38.0 | 28538 | 0.3331 | 0.8715 |
| 0.2471 | 39.0 | 29289 | 0.3324 | 0.8715 |
| 0.3456 | 40.0 | 30040 | 0.3317 | 0.8715 |
| 0.3078 | 41.0 | 30791 | 0.3311 | 0.8715 |
| 0.24 | 42.0 | 31542 | 0.3306 | 0.8715 |
| 0.289 | 43.0 | 32293 | 0.3302 | 0.8715 |
| 0.2977 | 44.0 | 33044 | 0.3297 | 0.8715 |
| 0.2559 | 45.0 | 33795 | 0.3294 | 0.8715 |
| 0.3508 | 46.0 | 34546 | 0.3292 | 0.8715 |
| 0.26 | 47.0 | 35297 | 0.3291 | 0.8715 |
| 0.3325 | 48.0 | 36048 | 0.3290 | 0.8715 |
| 0.2898 | 49.0 | 36799 | 0.3289 | 0.8715 |
| 0.2912 | 50.0 | 37550 | 0.3289 | 0.8715 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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