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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_00001_fold3
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: 0.91
---
<!-- 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_00001_fold3
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: 0.5920
- Accuracy: 0.91
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4085 | 1.0 | 75 | 0.3406 | 0.8733 |
| 0.3125 | 2.0 | 150 | 0.2766 | 0.905 |
| 0.272 | 3.0 | 225 | 0.2526 | 0.9117 |
| 0.2066 | 4.0 | 300 | 0.2426 | 0.9167 |
| 0.1315 | 5.0 | 375 | 0.2415 | 0.9233 |
| 0.1338 | 6.0 | 450 | 0.2667 | 0.9133 |
| 0.095 | 7.0 | 525 | 0.2679 | 0.9183 |
| 0.1144 | 8.0 | 600 | 0.2699 | 0.9267 |
| 0.038 | 9.0 | 675 | 0.2963 | 0.9183 |
| 0.0367 | 10.0 | 750 | 0.3153 | 0.925 |
| 0.0325 | 11.0 | 825 | 0.3378 | 0.92 |
| 0.0172 | 12.0 | 900 | 0.3441 | 0.9183 |
| 0.0285 | 13.0 | 975 | 0.3703 | 0.9217 |
| 0.0132 | 14.0 | 1050 | 0.3979 | 0.9117 |
| 0.0356 | 15.0 | 1125 | 0.3938 | 0.9167 |
| 0.0285 | 16.0 | 1200 | 0.4361 | 0.9117 |
| 0.0435 | 17.0 | 1275 | 0.4564 | 0.905 |
| 0.0412 | 18.0 | 1350 | 0.4606 | 0.905 |
| 0.0106 | 19.0 | 1425 | 0.4449 | 0.9133 |
| 0.0192 | 20.0 | 1500 | 0.4442 | 0.9167 |
| 0.0051 | 21.0 | 1575 | 0.4723 | 0.9117 |
| 0.0266 | 22.0 | 1650 | 0.5052 | 0.9117 |
| 0.0217 | 23.0 | 1725 | 0.4785 | 0.915 |
| 0.0019 | 24.0 | 1800 | 0.5058 | 0.9117 |
| 0.0069 | 25.0 | 1875 | 0.5124 | 0.91 |
| 0.0008 | 26.0 | 1950 | 0.5249 | 0.9117 |
| 0.0081 | 27.0 | 2025 | 0.5029 | 0.91 |
| 0.0213 | 28.0 | 2100 | 0.4919 | 0.9167 |
| 0.0025 | 29.0 | 2175 | 0.5055 | 0.9167 |
| 0.0366 | 30.0 | 2250 | 0.5226 | 0.9117 |
| 0.0192 | 31.0 | 2325 | 0.5652 | 0.91 |
| 0.0012 | 32.0 | 2400 | 0.5128 | 0.92 |
| 0.0191 | 33.0 | 2475 | 0.5580 | 0.9117 |
| 0.0168 | 34.0 | 2550 | 0.5615 | 0.905 |
| 0.0045 | 35.0 | 2625 | 0.5647 | 0.9133 |
| 0.0069 | 36.0 | 2700 | 0.5389 | 0.91 |
| 0.021 | 37.0 | 2775 | 0.5519 | 0.9133 |
| 0.0264 | 38.0 | 2850 | 0.5472 | 0.9117 |
| 0.0403 | 39.0 | 2925 | 0.5693 | 0.91 |
| 0.001 | 40.0 | 3000 | 0.5532 | 0.91 |
| 0.0004 | 41.0 | 3075 | 0.5673 | 0.9117 |
| 0.0344 | 42.0 | 3150 | 0.5624 | 0.9067 |
| 0.0221 | 43.0 | 3225 | 0.5673 | 0.91 |
| 0.0004 | 44.0 | 3300 | 0.5783 | 0.91 |
| 0.0156 | 45.0 | 3375 | 0.5833 | 0.9083 |
| 0.021 | 46.0 | 3450 | 0.5741 | 0.9117 |
| 0.0145 | 47.0 | 3525 | 0.5806 | 0.91 |
| 0.0049 | 48.0 | 3600 | 0.5891 | 0.91 |
| 0.0162 | 49.0 | 3675 | 0.5932 | 0.9083 |
| 0.0336 | 50.0 | 3750 | 0.5920 | 0.91 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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