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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_0001_fold4
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: 0.87
---
<!-- 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_0001_fold4
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: 1.1137
- Accuracy: 0.87
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3443 | 1.0 | 75 | 0.4137 | 0.8583 |
| 0.258 | 2.0 | 150 | 0.4036 | 0.8483 |
| 0.1343 | 3.0 | 225 | 0.4810 | 0.8533 |
| 0.0768 | 4.0 | 300 | 0.5625 | 0.86 |
| 0.0189 | 5.0 | 375 | 0.6619 | 0.8617 |
| 0.0435 | 6.0 | 450 | 0.6679 | 0.875 |
| 0.0162 | 7.0 | 525 | 0.7878 | 0.86 |
| 0.0677 | 8.0 | 600 | 0.7298 | 0.875 |
| 0.0423 | 9.0 | 675 | 0.8935 | 0.855 |
| 0.0172 | 10.0 | 750 | 0.8762 | 0.8717 |
| 0.001 | 11.0 | 825 | 0.8614 | 0.865 |
| 0.0092 | 12.0 | 900 | 0.8623 | 0.8717 |
| 0.0016 | 13.0 | 975 | 0.8916 | 0.87 |
| 0.0049 | 14.0 | 1050 | 0.8926 | 0.88 |
| 0.0101 | 15.0 | 1125 | 0.9303 | 0.8683 |
| 0.0014 | 16.0 | 1200 | 0.9140 | 0.8783 |
| 0.001 | 17.0 | 1275 | 0.9424 | 0.8817 |
| 0.0053 | 18.0 | 1350 | 0.8806 | 0.8817 |
| 0.0012 | 19.0 | 1425 | 0.9188 | 0.8917 |
| 0.0147 | 20.0 | 1500 | 0.9436 | 0.8767 |
| 0.0025 | 21.0 | 1575 | 0.9848 | 0.88 |
| 0.0092 | 22.0 | 1650 | 0.9945 | 0.8817 |
| 0.0279 | 23.0 | 1725 | 1.0063 | 0.875 |
| 0.0046 | 24.0 | 1800 | 1.0539 | 0.8767 |
| 0.0043 | 25.0 | 1875 | 1.0635 | 0.8717 |
| 0.0045 | 26.0 | 1950 | 1.0471 | 0.8733 |
| 0.0 | 27.0 | 2025 | 1.0128 | 0.8783 |
| 0.0004 | 28.0 | 2100 | 1.0296 | 0.8717 |
| 0.0001 | 29.0 | 2175 | 1.0117 | 0.875 |
| 0.0001 | 30.0 | 2250 | 1.0423 | 0.87 |
| 0.0073 | 31.0 | 2325 | 1.0722 | 0.87 |
| 0.0 | 32.0 | 2400 | 1.0662 | 0.8767 |
| 0.0 | 33.0 | 2475 | 1.0416 | 0.8717 |
| 0.0 | 34.0 | 2550 | 1.0959 | 0.8717 |
| 0.0034 | 35.0 | 2625 | 1.1220 | 0.87 |
| 0.0 | 36.0 | 2700 | 1.1441 | 0.8733 |
| 0.0 | 37.0 | 2775 | 1.1553 | 0.8733 |
| 0.0022 | 38.0 | 2850 | 1.1117 | 0.8767 |
| 0.0 | 39.0 | 2925 | 1.1002 | 0.8717 |
| 0.0 | 40.0 | 3000 | 1.1022 | 0.8683 |
| 0.003 | 41.0 | 3075 | 1.1129 | 0.8667 |
| 0.008 | 42.0 | 3150 | 1.1397 | 0.8667 |
| 0.0 | 43.0 | 3225 | 1.1224 | 0.87 |
| 0.0 | 44.0 | 3300 | 1.1186 | 0.8717 |
| 0.0 | 45.0 | 3375 | 1.1121 | 0.87 |
| 0.0001 | 46.0 | 3450 | 1.1134 | 0.87 |
| 0.0 | 47.0 | 3525 | 1.1172 | 0.8683 |
| 0.0001 | 48.0 | 3600 | 1.1134 | 0.87 |
| 0.0023 | 49.0 | 3675 | 1.1139 | 0.87 |
| 0.0022 | 50.0 | 3750 | 1.1137 | 0.87 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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