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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_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: 0.8964941569282137
---
<!-- 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_5x_beit_base_adamax_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0655
- Accuracy: 0.8965
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3493 | 1.0 | 376 | 0.3879 | 0.8581 |
| 0.2155 | 2.0 | 752 | 0.3019 | 0.8915 |
| 0.1776 | 3.0 | 1128 | 0.3875 | 0.8548 |
| 0.1242 | 4.0 | 1504 | 0.3809 | 0.8831 |
| 0.0896 | 5.0 | 1880 | 0.5028 | 0.8798 |
| 0.1253 | 6.0 | 2256 | 0.4979 | 0.8982 |
| 0.1104 | 7.0 | 2632 | 0.5865 | 0.8681 |
| 0.0316 | 8.0 | 3008 | 0.5613 | 0.8831 |
| 0.0721 | 9.0 | 3384 | 0.5293 | 0.8965 |
| 0.0201 | 10.0 | 3760 | 0.6272 | 0.8881 |
| 0.0359 | 11.0 | 4136 | 0.4934 | 0.8998 |
| 0.0744 | 12.0 | 4512 | 0.6114 | 0.8948 |
| 0.0347 | 13.0 | 4888 | 0.5456 | 0.9082 |
| 0.0311 | 14.0 | 5264 | 0.6131 | 0.8881 |
| 0.0168 | 15.0 | 5640 | 0.6543 | 0.8932 |
| 0.0168 | 16.0 | 6016 | 0.7183 | 0.8881 |
| 0.0016 | 17.0 | 6392 | 0.6732 | 0.8982 |
| 0.0267 | 18.0 | 6768 | 0.6217 | 0.9015 |
| 0.0052 | 19.0 | 7144 | 0.8606 | 0.8881 |
| 0.0397 | 20.0 | 7520 | 0.6236 | 0.8965 |
| 0.0267 | 21.0 | 7896 | 0.7627 | 0.8898 |
| 0.0186 | 22.0 | 8272 | 0.6922 | 0.8965 |
| 0.0249 | 23.0 | 8648 | 0.7332 | 0.8865 |
| 0.0032 | 24.0 | 9024 | 0.7665 | 0.8998 |
| 0.0275 | 25.0 | 9400 | 0.6785 | 0.8948 |
| 0.024 | 26.0 | 9776 | 0.7205 | 0.8915 |
| 0.0009 | 27.0 | 10152 | 0.7304 | 0.9015 |
| 0.0003 | 28.0 | 10528 | 0.7307 | 0.9065 |
| 0.0154 | 29.0 | 10904 | 0.7519 | 0.8965 |
| 0.0031 | 30.0 | 11280 | 0.8948 | 0.8932 |
| 0.0002 | 31.0 | 11656 | 0.8220 | 0.8998 |
| 0.0001 | 32.0 | 12032 | 0.7942 | 0.9048 |
| 0.0 | 33.0 | 12408 | 0.8498 | 0.9065 |
| 0.0055 | 34.0 | 12784 | 0.7753 | 0.8798 |
| 0.0001 | 35.0 | 13160 | 0.8717 | 0.8915 |
| 0.0 | 36.0 | 13536 | 0.9811 | 0.8865 |
| 0.0 | 37.0 | 13912 | 0.9556 | 0.8898 |
| 0.0003 | 38.0 | 14288 | 0.9804 | 0.8865 |
| 0.013 | 39.0 | 14664 | 0.9497 | 0.8965 |
| 0.0 | 40.0 | 15040 | 1.0094 | 0.8831 |
| 0.0 | 41.0 | 15416 | 0.9964 | 0.8881 |
| 0.0 | 42.0 | 15792 | 0.9367 | 0.8965 |
| 0.0 | 43.0 | 16168 | 1.0400 | 0.9015 |
| 0.0009 | 44.0 | 16544 | 1.0395 | 0.8948 |
| 0.0 | 45.0 | 16920 | 1.0420 | 0.8932 |
| 0.0031 | 46.0 | 17296 | 1.0873 | 0.8965 |
| 0.0 | 47.0 | 17672 | 1.0455 | 0.9032 |
| 0.0 | 48.0 | 18048 | 1.0612 | 0.8965 |
| 0.0 | 49.0 | 18424 | 1.0632 | 0.8998 |
| 0.0024 | 50.0 | 18800 | 1.0655 | 0.8965 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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