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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_fold5
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: 0.88
---
<!-- 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_fold5
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: 0.8148
- Accuracy: 0.88
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3159 | 1.0 | 75 | 0.2787 | 0.8933 |
| 0.2494 | 2.0 | 150 | 0.2824 | 0.8917 |
| 0.1709 | 3.0 | 225 | 0.2857 | 0.89 |
| 0.0771 | 4.0 | 300 | 0.3708 | 0.8933 |
| 0.0554 | 5.0 | 375 | 0.4256 | 0.895 |
| 0.0571 | 6.0 | 450 | 0.4870 | 0.8867 |
| 0.0043 | 7.0 | 525 | 0.5217 | 0.9017 |
| 0.0346 | 8.0 | 600 | 0.5838 | 0.8983 |
| 0.0305 | 9.0 | 675 | 0.5589 | 0.89 |
| 0.0299 | 10.0 | 750 | 0.6507 | 0.8833 |
| 0.0112 | 11.0 | 825 | 0.7257 | 0.885 |
| 0.0571 | 12.0 | 900 | 0.6425 | 0.8933 |
| 0.0111 | 13.0 | 975 | 0.6434 | 0.885 |
| 0.0007 | 14.0 | 1050 | 0.6590 | 0.8917 |
| 0.0158 | 15.0 | 1125 | 0.6659 | 0.895 |
| 0.0001 | 16.0 | 1200 | 0.6546 | 0.8983 |
| 0.0007 | 17.0 | 1275 | 0.6736 | 0.8867 |
| 0.0231 | 18.0 | 1350 | 0.7021 | 0.8917 |
| 0.0081 | 19.0 | 1425 | 0.7031 | 0.8917 |
| 0.0001 | 20.0 | 1500 | 0.7077 | 0.8833 |
| 0.0034 | 21.0 | 1575 | 0.6794 | 0.885 |
| 0.0184 | 22.0 | 1650 | 0.7927 | 0.865 |
| 0.0002 | 23.0 | 1725 | 0.7523 | 0.8783 |
| 0.0048 | 24.0 | 1800 | 0.7237 | 0.885 |
| 0.0065 | 25.0 | 1875 | 0.7425 | 0.8867 |
| 0.0064 | 26.0 | 1950 | 0.7940 | 0.8833 |
| 0.0055 | 27.0 | 2025 | 0.7223 | 0.8983 |
| 0.0092 | 28.0 | 2100 | 0.7594 | 0.8933 |
| 0.0 | 29.0 | 2175 | 0.7361 | 0.89 |
| 0.0 | 30.0 | 2250 | 0.7567 | 0.89 |
| 0.017 | 31.0 | 2325 | 0.7474 | 0.8883 |
| 0.0029 | 32.0 | 2400 | 0.8687 | 0.8767 |
| 0.0165 | 33.0 | 2475 | 0.8109 | 0.8883 |
| 0.0031 | 34.0 | 2550 | 0.8076 | 0.885 |
| 0.0039 | 35.0 | 2625 | 0.8393 | 0.8833 |
| 0.0031 | 36.0 | 2700 | 0.8234 | 0.8817 |
| 0.0001 | 37.0 | 2775 | 0.8155 | 0.8833 |
| 0.0034 | 38.0 | 2850 | 0.8110 | 0.89 |
| 0.0036 | 39.0 | 2925 | 0.8344 | 0.8817 |
| 0.0002 | 40.0 | 3000 | 0.8172 | 0.8833 |
| 0.0025 | 41.0 | 3075 | 0.8298 | 0.8817 |
| 0.0021 | 42.0 | 3150 | 0.8481 | 0.8817 |
| 0.0001 | 43.0 | 3225 | 0.8405 | 0.8817 |
| 0.0035 | 44.0 | 3300 | 0.8375 | 0.8833 |
| 0.0006 | 45.0 | 3375 | 0.8281 | 0.885 |
| 0.0024 | 46.0 | 3450 | 0.8226 | 0.8833 |
| 0.0 | 47.0 | 3525 | 0.8109 | 0.8817 |
| 0.0 | 48.0 | 3600 | 0.8113 | 0.88 |
| 0.0026 | 49.0 | 3675 | 0.8154 | 0.88 |
| 0.0067 | 50.0 | 3750 | 0.8148 | 0.88 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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