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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_1x_beit_base_adamax_001_fold2
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: 0.7903494176372712
---
<!-- 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_1x_beit_base_adamax_001_fold2
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: 1.4865
- Accuracy: 0.7903
## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9007 | 1.0 | 75 | 0.8802 | 0.5191 |
| 0.7789 | 2.0 | 150 | 0.8973 | 0.5424 |
| 0.8219 | 3.0 | 225 | 0.7607 | 0.6406 |
| 0.7838 | 4.0 | 300 | 0.7358 | 0.6522 |
| 0.6602 | 5.0 | 375 | 0.6978 | 0.6672 |
| 0.7026 | 6.0 | 450 | 0.6685 | 0.6955 |
| 0.6394 | 7.0 | 525 | 0.7731 | 0.6589 |
| 0.6471 | 8.0 | 600 | 0.6234 | 0.7138 |
| 0.5881 | 9.0 | 675 | 0.6358 | 0.7205 |
| 0.5254 | 10.0 | 750 | 0.5746 | 0.7671 |
| 0.5153 | 11.0 | 825 | 0.5501 | 0.7704 |
| 0.5459 | 12.0 | 900 | 0.5543 | 0.7687 |
| 0.5526 | 13.0 | 975 | 0.5321 | 0.7737 |
| 0.5236 | 14.0 | 1050 | 0.5404 | 0.7937 |
| 0.4317 | 15.0 | 1125 | 0.6220 | 0.7604 |
| 0.4195 | 16.0 | 1200 | 0.5679 | 0.7854 |
| 0.3753 | 17.0 | 1275 | 0.6021 | 0.7687 |
| 0.3821 | 18.0 | 1350 | 0.5958 | 0.7854 |
| 0.3599 | 19.0 | 1425 | 0.6478 | 0.7837 |
| 0.2813 | 20.0 | 1500 | 0.6634 | 0.7671 |
| 0.224 | 21.0 | 1575 | 0.6766 | 0.7820 |
| 0.2635 | 22.0 | 1650 | 0.6781 | 0.7870 |
| 0.1832 | 23.0 | 1725 | 0.8041 | 0.7604 |
| 0.1751 | 24.0 | 1800 | 0.8069 | 0.7671 |
| 0.2421 | 25.0 | 1875 | 0.8820 | 0.7737 |
| 0.2115 | 26.0 | 1950 | 0.8838 | 0.7970 |
| 0.1798 | 27.0 | 2025 | 0.8954 | 0.7787 |
| 0.1341 | 28.0 | 2100 | 1.0505 | 0.7987 |
| 0.0669 | 29.0 | 2175 | 1.2992 | 0.7770 |
| 0.0892 | 30.0 | 2250 | 1.1168 | 0.7987 |
| 0.1159 | 31.0 | 2325 | 1.2066 | 0.7870 |
| 0.1289 | 32.0 | 2400 | 1.5859 | 0.7687 |
| 0.0687 | 33.0 | 2475 | 1.1777 | 0.7887 |
| 0.0226 | 34.0 | 2550 | 1.4423 | 0.7854 |
| 0.04 | 35.0 | 2625 | 1.4594 | 0.7870 |
| 0.0552 | 36.0 | 2700 | 1.3867 | 0.7820 |
| 0.0439 | 37.0 | 2775 | 1.4599 | 0.7720 |
| 0.0308 | 38.0 | 2850 | 1.4968 | 0.7903 |
| 0.0564 | 39.0 | 2925 | 1.5256 | 0.7953 |
| 0.0227 | 40.0 | 3000 | 1.4454 | 0.7953 |
| 0.0214 | 41.0 | 3075 | 1.3100 | 0.8087 |
| 0.0167 | 42.0 | 3150 | 1.4699 | 0.7987 |
| 0.0299 | 43.0 | 3225 | 1.4525 | 0.7903 |
| 0.0171 | 44.0 | 3300 | 1.3889 | 0.8053 |
| 0.011 | 45.0 | 3375 | 1.3819 | 0.7920 |
| 0.014 | 46.0 | 3450 | 1.5122 | 0.7903 |
| 0.0198 | 47.0 | 3525 | 1.4328 | 0.7920 |
| 0.0085 | 48.0 | 3600 | 1.5057 | 0.7920 |
| 0.0028 | 49.0 | 3675 | 1.4856 | 0.7903 |
| 0.0049 | 50.0 | 3750 | 1.4865 | 0.7903 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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