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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_00001_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: 0.6483333333333333
---
<!-- 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_00001_fold4
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: 0.7900
- Accuracy: 0.6483
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2211 | 1.0 | 750 | 1.1931 | 0.3533 |
| 1.1323 | 2.0 | 1500 | 1.1612 | 0.365 |
| 1.184 | 3.0 | 2250 | 1.1336 | 0.37 |
| 1.1136 | 4.0 | 3000 | 1.1091 | 0.3817 |
| 0.9758 | 5.0 | 3750 | 1.0870 | 0.385 |
| 1.0842 | 6.0 | 4500 | 1.0669 | 0.3917 |
| 1.0165 | 7.0 | 5250 | 1.0484 | 0.4117 |
| 1.0062 | 8.0 | 6000 | 1.0310 | 0.43 |
| 1.0015 | 9.0 | 6750 | 1.0148 | 0.4367 |
| 0.9415 | 10.0 | 7500 | 0.9997 | 0.45 |
| 0.9588 | 11.0 | 8250 | 0.9856 | 0.4533 |
| 0.9674 | 12.0 | 9000 | 0.9724 | 0.47 |
| 0.9046 | 13.0 | 9750 | 0.9600 | 0.4733 |
| 0.9542 | 14.0 | 10500 | 0.9483 | 0.4867 |
| 0.8663 | 15.0 | 11250 | 0.9372 | 0.5 |
| 0.8717 | 16.0 | 12000 | 0.9268 | 0.51 |
| 0.7922 | 17.0 | 12750 | 0.9171 | 0.525 |
| 0.8562 | 18.0 | 13500 | 0.9078 | 0.535 |
| 0.9212 | 19.0 | 14250 | 0.8991 | 0.5433 |
| 0.8823 | 20.0 | 15000 | 0.8907 | 0.5567 |
| 0.8498 | 21.0 | 15750 | 0.8828 | 0.565 |
| 0.8335 | 22.0 | 16500 | 0.8754 | 0.575 |
| 0.8369 | 23.0 | 17250 | 0.8683 | 0.5867 |
| 0.8886 | 24.0 | 18000 | 0.8617 | 0.5917 |
| 0.8131 | 25.0 | 18750 | 0.8555 | 0.6 |
| 0.8107 | 26.0 | 19500 | 0.8497 | 0.605 |
| 0.7489 | 27.0 | 20250 | 0.8442 | 0.61 |
| 0.8154 | 28.0 | 21000 | 0.8390 | 0.6167 |
| 0.7935 | 29.0 | 21750 | 0.8341 | 0.62 |
| 0.7606 | 30.0 | 22500 | 0.8296 | 0.6267 |
| 0.7688 | 31.0 | 23250 | 0.8253 | 0.6283 |
| 0.755 | 32.0 | 24000 | 0.8214 | 0.63 |
| 0.8046 | 33.0 | 24750 | 0.8176 | 0.63 |
| 0.8193 | 34.0 | 25500 | 0.8142 | 0.6317 |
| 0.7668 | 35.0 | 26250 | 0.8110 | 0.635 |
| 0.7573 | 36.0 | 27000 | 0.8080 | 0.6367 |
| 0.7928 | 37.0 | 27750 | 0.8053 | 0.6417 |
| 0.792 | 38.0 | 28500 | 0.8028 | 0.6417 |
| 0.7917 | 39.0 | 29250 | 0.8007 | 0.645 |
| 0.7521 | 40.0 | 30000 | 0.7987 | 0.645 |
| 0.777 | 41.0 | 30750 | 0.7969 | 0.6483 |
| 0.7956 | 42.0 | 31500 | 0.7954 | 0.6483 |
| 0.8067 | 43.0 | 32250 | 0.7940 | 0.65 |
| 0.7335 | 44.0 | 33000 | 0.7929 | 0.65 |
| 0.7708 | 45.0 | 33750 | 0.7920 | 0.6483 |
| 0.74 | 46.0 | 34500 | 0.7912 | 0.6483 |
| 0.7222 | 47.0 | 35250 | 0.7906 | 0.6483 |
| 0.7572 | 48.0 | 36000 | 0.7902 | 0.6483 |
| 0.7909 | 49.0 | 36750 | 0.7900 | 0.6483 |
| 0.7055 | 50.0 | 37500 | 0.7900 | 0.6483 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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