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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_00001_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: 0.8985024958402662
---
<!-- 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_00001_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: 0.8394
- Accuracy: 0.8985
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3036 | 1.0 | 375 | 0.2703 | 0.8918 |
| 0.2118 | 2.0 | 750 | 0.2674 | 0.8968 |
| 0.1557 | 3.0 | 1125 | 0.2889 | 0.8918 |
| 0.074 | 4.0 | 1500 | 0.2842 | 0.9002 |
| 0.0616 | 5.0 | 1875 | 0.3403 | 0.8935 |
| 0.036 | 6.0 | 2250 | 0.3534 | 0.9101 |
| 0.0382 | 7.0 | 2625 | 0.4309 | 0.8985 |
| 0.0686 | 8.0 | 3000 | 0.4834 | 0.8985 |
| 0.022 | 9.0 | 3375 | 0.5298 | 0.8935 |
| 0.0159 | 10.0 | 3750 | 0.5866 | 0.8985 |
| 0.0173 | 11.0 | 4125 | 0.5610 | 0.8968 |
| 0.0241 | 12.0 | 4500 | 0.6962 | 0.8869 |
| 0.0123 | 13.0 | 4875 | 0.6252 | 0.8952 |
| 0.0054 | 14.0 | 5250 | 0.6170 | 0.9002 |
| 0.0251 | 15.0 | 5625 | 0.6453 | 0.8952 |
| 0.0003 | 16.0 | 6000 | 0.6804 | 0.8952 |
| 0.0563 | 17.0 | 6375 | 0.6912 | 0.8985 |
| 0.0079 | 18.0 | 6750 | 0.6905 | 0.9018 |
| 0.0009 | 19.0 | 7125 | 0.7171 | 0.8935 |
| 0.0206 | 20.0 | 7500 | 0.7602 | 0.8985 |
| 0.0222 | 21.0 | 7875 | 0.7242 | 0.8952 |
| 0.0005 | 22.0 | 8250 | 0.7227 | 0.9002 |
| 0.0001 | 23.0 | 8625 | 0.7725 | 0.9002 |
| 0.0002 | 24.0 | 9000 | 0.7700 | 0.8935 |
| 0.0001 | 25.0 | 9375 | 0.7746 | 0.8985 |
| 0.0001 | 26.0 | 9750 | 0.7609 | 0.9018 |
| 0.017 | 27.0 | 10125 | 0.8256 | 0.8918 |
| 0.0019 | 28.0 | 10500 | 0.7444 | 0.8952 |
| 0.0254 | 29.0 | 10875 | 0.7839 | 0.9035 |
| 0.0041 | 30.0 | 11250 | 0.7929 | 0.9002 |
| 0.0018 | 31.0 | 11625 | 0.7983 | 0.8968 |
| 0.0163 | 32.0 | 12000 | 0.8337 | 0.8968 |
| 0.0122 | 33.0 | 12375 | 0.8065 | 0.8918 |
| 0.0021 | 34.0 | 12750 | 0.8472 | 0.8968 |
| 0.0003 | 35.0 | 13125 | 0.8572 | 0.8968 |
| 0.0036 | 36.0 | 13500 | 0.8680 | 0.8935 |
| 0.0086 | 37.0 | 13875 | 0.8533 | 0.8935 |
| 0.0002 | 38.0 | 14250 | 0.8606 | 0.8885 |
| 0.0065 | 39.0 | 14625 | 0.8465 | 0.8869 |
| 0.0212 | 40.0 | 15000 | 0.8444 | 0.8952 |
| 0.0163 | 41.0 | 15375 | 0.8576 | 0.8918 |
| 0.0071 | 42.0 | 15750 | 0.8227 | 0.8952 |
| 0.0234 | 43.0 | 16125 | 0.8305 | 0.8935 |
| 0.0019 | 44.0 | 16500 | 0.8174 | 0.9002 |
| 0.0226 | 45.0 | 16875 | 0.8559 | 0.8902 |
| 0.0176 | 46.0 | 17250 | 0.8405 | 0.8918 |
| 0.0236 | 47.0 | 17625 | 0.8413 | 0.8952 |
| 0.0179 | 48.0 | 18000 | 0.8437 | 0.8985 |
| 0.0141 | 49.0 | 18375 | 0.8368 | 0.8968 |
| 0.0007 | 50.0 | 18750 | 0.8394 | 0.8985 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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