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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: hushem_5x_beit_base_adamax_001_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: 0.6976744186046512
---
<!-- 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. -->
# hushem_5x_beit_base_adamax_001_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: 2.7065
- Accuracy: 0.6977
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4764 | 1.0 | 28 | 1.3308 | 0.4651 |
| 1.3399 | 2.0 | 56 | 1.2727 | 0.5116 |
| 1.2309 | 3.0 | 84 | 1.0539 | 0.5349 |
| 1.0517 | 4.0 | 112 | 1.0384 | 0.6279 |
| 0.9736 | 5.0 | 140 | 1.1313 | 0.5349 |
| 1.0108 | 6.0 | 168 | 0.6569 | 0.8140 |
| 0.9287 | 7.0 | 196 | 0.7779 | 0.7674 |
| 0.9063 | 8.0 | 224 | 0.8802 | 0.5581 |
| 0.7994 | 9.0 | 252 | 1.1244 | 0.5581 |
| 0.8319 | 10.0 | 280 | 0.7284 | 0.7209 |
| 0.8096 | 11.0 | 308 | 0.7775 | 0.7209 |
| 0.8274 | 12.0 | 336 | 0.7683 | 0.6744 |
| 0.798 | 13.0 | 364 | 0.8219 | 0.6512 |
| 0.6756 | 14.0 | 392 | 0.5656 | 0.7442 |
| 0.9098 | 15.0 | 420 | 0.6922 | 0.6279 |
| 0.6261 | 16.0 | 448 | 1.0949 | 0.5814 |
| 0.6243 | 17.0 | 476 | 0.7154 | 0.7209 |
| 0.7247 | 18.0 | 504 | 0.6429 | 0.7674 |
| 0.6106 | 19.0 | 532 | 0.7927 | 0.6744 |
| 0.4831 | 20.0 | 560 | 0.6060 | 0.7674 |
| 0.5359 | 21.0 | 588 | 1.2593 | 0.5349 |
| 0.429 | 22.0 | 616 | 1.0232 | 0.6744 |
| 0.4536 | 23.0 | 644 | 1.2564 | 0.6744 |
| 0.2969 | 24.0 | 672 | 1.2153 | 0.6279 |
| 0.3018 | 25.0 | 700 | 1.3650 | 0.5814 |
| 0.2695 | 26.0 | 728 | 1.6759 | 0.6279 |
| 0.2235 | 27.0 | 756 | 1.8158 | 0.5814 |
| 0.2674 | 28.0 | 784 | 1.7475 | 0.6977 |
| 0.1711 | 29.0 | 812 | 1.5630 | 0.7209 |
| 0.1241 | 30.0 | 840 | 1.5976 | 0.7442 |
| 0.1378 | 31.0 | 868 | 1.8498 | 0.7209 |
| 0.1016 | 32.0 | 896 | 2.3022 | 0.6279 |
| 0.1245 | 33.0 | 924 | 2.0178 | 0.6047 |
| 0.1029 | 34.0 | 952 | 2.0725 | 0.6744 |
| 0.0329 | 35.0 | 980 | 1.6046 | 0.7674 |
| 0.1038 | 36.0 | 1008 | 2.3364 | 0.6047 |
| 0.055 | 37.0 | 1036 | 3.1044 | 0.5581 |
| 0.0031 | 38.0 | 1064 | 2.6896 | 0.6512 |
| 0.0537 | 39.0 | 1092 | 3.2350 | 0.6047 |
| 0.0484 | 40.0 | 1120 | 3.5002 | 0.5814 |
| 0.0311 | 41.0 | 1148 | 3.0948 | 0.6512 |
| 0.0491 | 42.0 | 1176 | 2.8268 | 0.6977 |
| 0.0023 | 43.0 | 1204 | 2.5306 | 0.6977 |
| 0.0192 | 44.0 | 1232 | 2.3977 | 0.6977 |
| 0.0339 | 45.0 | 1260 | 2.5488 | 0.6977 |
| 0.0369 | 46.0 | 1288 | 2.5878 | 0.7209 |
| 0.049 | 47.0 | 1316 | 2.7159 | 0.6977 |
| 0.0044 | 48.0 | 1344 | 2.7074 | 0.6977 |
| 0.0183 | 49.0 | 1372 | 2.7065 | 0.6977 |
| 0.0409 | 50.0 | 1400 | 2.7065 | 0.6977 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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