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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_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: 0.6316666666666667
---
<!-- 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_fold3
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: 0.8246
- Accuracy: 0.6317
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2284 | 1.0 | 750 | 1.2241 | 0.3517 |
| 1.1457 | 2.0 | 1500 | 1.1930 | 0.365 |
| 1.1396 | 3.0 | 2250 | 1.1661 | 0.3783 |
| 1.0897 | 4.0 | 3000 | 1.1425 | 0.385 |
| 1.025 | 5.0 | 3750 | 1.1215 | 0.3883 |
| 1.0158 | 6.0 | 4500 | 1.1022 | 0.3883 |
| 0.9975 | 7.0 | 5250 | 1.0842 | 0.4017 |
| 1.0278 | 8.0 | 6000 | 1.0673 | 0.4067 |
| 0.9784 | 9.0 | 6750 | 1.0514 | 0.4133 |
| 0.9157 | 10.0 | 7500 | 1.0366 | 0.4317 |
| 0.9554 | 11.0 | 8250 | 1.0228 | 0.4467 |
| 0.8899 | 12.0 | 9000 | 1.0096 | 0.4667 |
| 0.9379 | 13.0 | 9750 | 0.9973 | 0.4767 |
| 0.944 | 14.0 | 10500 | 0.9856 | 0.4867 |
| 0.9071 | 15.0 | 11250 | 0.9745 | 0.4983 |
| 0.8922 | 16.0 | 12000 | 0.9641 | 0.505 |
| 0.8643 | 17.0 | 12750 | 0.9544 | 0.5133 |
| 0.8278 | 18.0 | 13500 | 0.9449 | 0.52 |
| 0.9039 | 19.0 | 14250 | 0.9361 | 0.5317 |
| 0.8559 | 20.0 | 15000 | 0.9279 | 0.5383 |
| 0.8179 | 21.0 | 15750 | 0.9199 | 0.545 |
| 0.8248 | 22.0 | 16500 | 0.9124 | 0.56 |
| 0.8379 | 23.0 | 17250 | 0.9052 | 0.56 |
| 0.864 | 24.0 | 18000 | 0.8985 | 0.565 |
| 0.8458 | 25.0 | 18750 | 0.8922 | 0.575 |
| 0.8014 | 26.0 | 19500 | 0.8861 | 0.5783 |
| 0.7589 | 27.0 | 20250 | 0.8805 | 0.5883 |
| 0.8089 | 28.0 | 21000 | 0.8752 | 0.595 |
| 0.8337 | 29.0 | 21750 | 0.8701 | 0.5983 |
| 0.7734 | 30.0 | 22500 | 0.8654 | 0.6033 |
| 0.7463 | 31.0 | 23250 | 0.8610 | 0.6033 |
| 0.7746 | 32.0 | 24000 | 0.8569 | 0.6067 |
| 0.8126 | 33.0 | 24750 | 0.8532 | 0.6117 |
| 0.7894 | 34.0 | 25500 | 0.8496 | 0.615 |
| 0.7634 | 35.0 | 26250 | 0.8463 | 0.615 |
| 0.7765 | 36.0 | 27000 | 0.8433 | 0.6167 |
| 0.8136 | 37.0 | 27750 | 0.8405 | 0.6217 |
| 0.8117 | 38.0 | 28500 | 0.8380 | 0.6217 |
| 0.7707 | 39.0 | 29250 | 0.8357 | 0.6217 |
| 0.7678 | 40.0 | 30000 | 0.8337 | 0.6267 |
| 0.7823 | 41.0 | 30750 | 0.8319 | 0.6283 |
| 0.7728 | 42.0 | 31500 | 0.8303 | 0.63 |
| 0.7705 | 43.0 | 32250 | 0.8289 | 0.6283 |
| 0.7342 | 44.0 | 33000 | 0.8277 | 0.6283 |
| 0.7107 | 45.0 | 33750 | 0.8267 | 0.6283 |
| 0.7263 | 46.0 | 34500 | 0.8259 | 0.63 |
| 0.7101 | 47.0 | 35250 | 0.8253 | 0.63 |
| 0.7724 | 48.0 | 36000 | 0.8249 | 0.6317 |
| 0.7714 | 49.0 | 36750 | 0.8247 | 0.6317 |
| 0.7461 | 50.0 | 37500 | 0.8246 | 0.6317 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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