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---
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license: apache-2.0
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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-large-patch16_fold2
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: test
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6483783783783784
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-large-patch16_fold2
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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.6891
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- Accuracy: 0.6484
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.2166 | 1.0 | 923 | 1.1497 | 0.6165 |
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| 0.9097 | 2.0 | 1846 | 1.0420 | 0.6449 |
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| 0.7046 | 3.0 | 2769 | 1.0073 | 0.6589 |
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| 0.4498 | 4.0 | 3692 | 1.0708 | 0.6619 |
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| 0.3558 | 5.0 | 4615 | 1.1984 | 0.6481 |
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| 0.2479 | 6.0 | 5538 | 1.3200 | 0.6535 |
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| 0.136 | 7.0 | 6461 | 1.4614 | 0.6508 |
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| 0.1353 | 8.0 | 7384 | 1.5768 | 0.6551 |
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| 0.0896 | 9.0 | 8307 | 1.6599 | 0.65 |
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| 0.1222 | 10.0 | 9230 | 1.6891 | 0.6484 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.0
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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