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update model card README.md
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README.md
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---
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license: apache-2.0
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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: beit-base-patch16-224-pt22k-ft22k-finetunedt
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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: train
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split: train
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args: train
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metrics:
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- name: Accuracy
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type: accuracy
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value: 1.0
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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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# beit-base-patch16-224-pt22k-ft22k-finetunedt
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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: 0.0000
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- Accuracy: 1.0
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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: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 15
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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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| 0.7369 | 1.0 | 25 | 0.0425 | 0.9972 |
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| 0.007 | 2.0 | 50 | 0.0005 | 1.0 |
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| 0.0041 | 3.0 | 75 | 0.0003 | 1.0 |
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| 0.0011 | 4.0 | 100 | 0.0002 | 1.0 |
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| 0.0008 | 5.0 | 125 | 0.0001 | 1.0 |
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| 0.0055 | 6.0 | 150 | 0.0002 | 1.0 |
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| 0.0007 | 7.0 | 175 | 0.0001 | 1.0 |
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| 0.0047 | 8.0 | 200 | 0.0001 | 1.0 |
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| 0.0005 | 9.0 | 225 | 0.0001 | 1.0 |
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| 0.006 | 10.0 | 250 | 0.0001 | 1.0 |
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| 0.0065 | 11.0 | 275 | 0.0001 | 1.0 |
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| 0.0023 | 12.0 | 300 | 0.0001 | 1.0 |
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| 0.0003 | 13.0 | 325 | 0.0001 | 1.0 |
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| 0.0011 | 14.0 | 350 | 0.0000 | 1.0 |
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| 0.0003 | 15.0 | 375 | 0.0000 | 1.0 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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