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license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_40x_beit_base_f3
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: 0.8837209302325582
---
<!-- 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_40x_beit_base_f3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9422
- Accuracy: 0.8837
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0594 | 1.0 | 108 | 0.4441 | 0.8372 |
| 0.0595 | 2.0 | 217 | 0.8725 | 0.8605 |
| 0.0173 | 3.0 | 325 | 0.5866 | 0.9070 |
| 0.0084 | 4.0 | 434 | 0.6360 | 0.8605 |
| 0.0005 | 5.0 | 542 | 0.6191 | 0.8837 |
| 0.0008 | 6.0 | 651 | 0.6635 | 0.9070 |
| 0.0008 | 7.0 | 759 | 0.8772 | 0.8837 |
| 0.0001 | 8.0 | 868 | 0.8012 | 0.9070 |
| 0.0 | 9.0 | 976 | 0.9139 | 0.8837 |
| 0.0001 | 9.95 | 1080 | 0.9422 | 0.8837 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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