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---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_beit_large_sgd_0001_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: 0.8768718801996672
---
<!-- 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_10x_beit_large_sgd_0001_fold2
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3022
- Accuracy: 0.8769
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9337 | 1.0 | 750 | 0.9902 | 0.5025 |
| 0.7559 | 2.0 | 1500 | 0.8323 | 0.6206 |
| 0.6418 | 3.0 | 2250 | 0.7119 | 0.7205 |
| 0.6498 | 4.0 | 3000 | 0.6261 | 0.7737 |
| 0.5308 | 5.0 | 3750 | 0.5616 | 0.8020 |
| 0.5189 | 6.0 | 4500 | 0.5157 | 0.8186 |
| 0.4977 | 7.0 | 5250 | 0.4808 | 0.8303 |
| 0.4495 | 8.0 | 6000 | 0.4552 | 0.8369 |
| 0.4544 | 9.0 | 6750 | 0.4332 | 0.8303 |
| 0.4325 | 10.0 | 7500 | 0.4166 | 0.8336 |
| 0.4708 | 11.0 | 8250 | 0.4025 | 0.8419 |
| 0.4375 | 12.0 | 9000 | 0.3904 | 0.8419 |
| 0.3875 | 13.0 | 9750 | 0.3796 | 0.8486 |
| 0.338 | 14.0 | 10500 | 0.3718 | 0.8486 |
| 0.3613 | 15.0 | 11250 | 0.3643 | 0.8502 |
| 0.3159 | 16.0 | 12000 | 0.3576 | 0.8569 |
| 0.313 | 17.0 | 12750 | 0.3520 | 0.8602 |
| 0.3243 | 18.0 | 13500 | 0.3466 | 0.8619 |
| 0.3747 | 19.0 | 14250 | 0.3420 | 0.8619 |
| 0.3494 | 20.0 | 15000 | 0.3382 | 0.8652 |
| 0.3628 | 21.0 | 15750 | 0.3347 | 0.8652 |
| 0.2681 | 22.0 | 16500 | 0.3313 | 0.8686 |
| 0.3103 | 23.0 | 17250 | 0.3283 | 0.8686 |
| 0.3029 | 24.0 | 18000 | 0.3255 | 0.8686 |
| 0.3439 | 25.0 | 18750 | 0.3228 | 0.8686 |
| 0.363 | 26.0 | 19500 | 0.3205 | 0.8735 |
| 0.3457 | 27.0 | 20250 | 0.3186 | 0.8735 |
| 0.3118 | 28.0 | 21000 | 0.3168 | 0.8719 |
| 0.3203 | 29.0 | 21750 | 0.3151 | 0.8719 |
| 0.2897 | 30.0 | 22500 | 0.3135 | 0.8702 |
| 0.3287 | 31.0 | 23250 | 0.3118 | 0.8702 |
| 0.3672 | 32.0 | 24000 | 0.3107 | 0.8719 |
| 0.3139 | 33.0 | 24750 | 0.3101 | 0.8702 |
| 0.3173 | 34.0 | 25500 | 0.3088 | 0.8719 |
| 0.3321 | 35.0 | 26250 | 0.3079 | 0.8735 |
| 0.3146 | 36.0 | 27000 | 0.3071 | 0.8735 |
| 0.3221 | 37.0 | 27750 | 0.3062 | 0.8735 |
| 0.2973 | 38.0 | 28500 | 0.3058 | 0.8752 |
| 0.275 | 39.0 | 29250 | 0.3050 | 0.8752 |
| 0.3603 | 40.0 | 30000 | 0.3045 | 0.8752 |
| 0.3249 | 41.0 | 30750 | 0.3040 | 0.8752 |
| 0.3107 | 42.0 | 31500 | 0.3036 | 0.8752 |
| 0.2783 | 43.0 | 32250 | 0.3032 | 0.8752 |
| 0.2901 | 44.0 | 33000 | 0.3029 | 0.8752 |
| 0.3257 | 45.0 | 33750 | 0.3026 | 0.8752 |
| 0.2732 | 46.0 | 34500 | 0.3025 | 0.8752 |
| 0.3622 | 47.0 | 35250 | 0.3024 | 0.8769 |
| 0.3082 | 48.0 | 36000 | 0.3023 | 0.8769 |
| 0.2937 | 49.0 | 36750 | 0.3022 | 0.8769 |
| 0.3097 | 50.0 | 37500 | 0.3022 | 0.8769 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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