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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_00001_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: 0.6360601001669449
---
<!-- 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_00001_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: 0.8308
- Accuracy: 0.6361
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1798 | 1.0 | 751 | 1.2530 | 0.3139 |
| 1.1588 | 2.0 | 1502 | 1.2210 | 0.3272 |
| 1.0286 | 3.0 | 2253 | 1.1929 | 0.3272 |
| 1.0699 | 4.0 | 3004 | 1.1680 | 0.3372 |
| 1.0532 | 5.0 | 3755 | 1.1455 | 0.3539 |
| 1.0168 | 6.0 | 4506 | 1.1249 | 0.3589 |
| 1.0334 | 7.0 | 5257 | 1.1059 | 0.3840 |
| 1.006 | 8.0 | 6008 | 1.0879 | 0.3923 |
| 0.9781 | 9.0 | 6759 | 1.0713 | 0.4073 |
| 0.9206 | 10.0 | 7510 | 1.0557 | 0.4324 |
| 0.9599 | 11.0 | 8261 | 1.0410 | 0.4457 |
| 0.8538 | 12.0 | 9012 | 1.0272 | 0.4591 |
| 0.8992 | 13.0 | 9763 | 1.0143 | 0.4725 |
| 0.9105 | 14.0 | 10514 | 1.0019 | 0.4925 |
| 0.8886 | 15.0 | 11265 | 0.9904 | 0.5058 |
| 0.8635 | 16.0 | 12016 | 0.9792 | 0.5209 |
| 0.9091 | 17.0 | 12767 | 0.9687 | 0.5292 |
| 0.8236 | 18.0 | 13518 | 0.9588 | 0.5342 |
| 0.8559 | 19.0 | 14269 | 0.9493 | 0.5426 |
| 0.7879 | 20.0 | 15020 | 0.9403 | 0.5509 |
| 0.765 | 21.0 | 15771 | 0.9320 | 0.5543 |
| 0.8223 | 22.0 | 16522 | 0.9238 | 0.5593 |
| 0.782 | 23.0 | 17273 | 0.9162 | 0.5659 |
| 0.875 | 24.0 | 18024 | 0.9090 | 0.5726 |
| 0.8022 | 25.0 | 18775 | 0.9023 | 0.5793 |
| 0.8471 | 26.0 | 19526 | 0.8959 | 0.5860 |
| 0.7822 | 27.0 | 20277 | 0.8898 | 0.5977 |
| 0.789 | 28.0 | 21028 | 0.8841 | 0.6010 |
| 0.8149 | 29.0 | 21779 | 0.8788 | 0.6027 |
| 0.7987 | 30.0 | 22530 | 0.8738 | 0.6077 |
| 0.7188 | 31.0 | 23281 | 0.8692 | 0.6160 |
| 0.802 | 32.0 | 24032 | 0.8649 | 0.6194 |
| 0.8114 | 33.0 | 24783 | 0.8608 | 0.6194 |
| 0.7414 | 34.0 | 25534 | 0.8570 | 0.6210 |
| 0.766 | 35.0 | 26285 | 0.8536 | 0.6210 |
| 0.7537 | 36.0 | 27036 | 0.8504 | 0.6260 |
| 0.7794 | 37.0 | 27787 | 0.8475 | 0.6277 |
| 0.7455 | 38.0 | 28538 | 0.8448 | 0.6311 |
| 0.7702 | 39.0 | 29289 | 0.8424 | 0.6311 |
| 0.75 | 40.0 | 30040 | 0.8403 | 0.6311 |
| 0.7442 | 41.0 | 30791 | 0.8384 | 0.6344 |
| 0.6885 | 42.0 | 31542 | 0.8367 | 0.6344 |
| 0.7317 | 43.0 | 32293 | 0.8353 | 0.6344 |
| 0.7377 | 44.0 | 33044 | 0.8340 | 0.6344 |
| 0.7327 | 45.0 | 33795 | 0.8330 | 0.6344 |
| 0.752 | 46.0 | 34546 | 0.8322 | 0.6361 |
| 0.7091 | 47.0 | 35297 | 0.8315 | 0.6361 |
| 0.7684 | 48.0 | 36048 | 0.8311 | 0.6361 |
| 0.7425 | 49.0 | 36799 | 0.8309 | 0.6361 |
| 0.7641 | 50.0 | 37550 | 0.8308 | 0.6361 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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