End of training
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README.md
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---
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license: apache-2.0
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base_model: microsoft/beit-large-patch16-224
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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_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold4
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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: 0.8420054200542005
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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_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold4
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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.
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It achieves the following results on the evaluation set:
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- Loss: 1.7296
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- Accuracy: 0.8420
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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: 0.0001
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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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| 0.3707 | 1.0 | 923 | 0.4482 | 0.8136 |
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| 0.3226 | 2.0 | 1846 | 0.4118 | 0.8336 |
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| 0.1875 | 3.0 | 2769 | 0.5879 | 0.8344 |
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| 0.1605 | 4.0 | 3692 | 0.7513 | 0.8304 |
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| 0.1167 | 5.0 | 4615 | 1.2298 | 0.8396 |
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| 0.0003 | 6.0 | 5538 | 1.5337 | 0.8331 |
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| 0.0635 | 7.0 | 6461 | 1.5787 | 0.8377 |
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| 0.0071 | 8.0 | 7384 | 1.6914 | 0.8350 |
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| 0.0 | 9.0 | 8307 | 1.7195 | 0.8404 |
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| 0.0 | 10.0 | 9230 | 1.7296 | 0.8420 |
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.0.1
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- Datasets 2.12.0
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- Tokenizers 0.13.2
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