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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: vit-base-patch16-224-finetuned-main-gpu-30e-final
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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: validation
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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: 0.9938775510204082
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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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# vit-base-patch16-224-finetuned-main-gpu-30e-final
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0211
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- Accuracy: 0.9939
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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: 30
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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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| 0.5113 | 1.0 | 551 | 0.4745 | 0.7971 |
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| 0.3409 | 2.0 | 1102 | 0.2697 | 0.8961 |
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| 0.2675 | 3.0 | 1653 | 0.1611 | 0.9381 |
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| 0.2092 | 4.0 | 2204 | 0.1176 | 0.9548 |
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| 0.2008 | 5.0 | 2755 | 0.0889 | 0.9656 |
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| 0.1555 | 6.0 | 3306 | 0.0666 | 0.9759 |
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| 0.1614 | 7.0 | 3857 | 0.0576 | 0.9778 |
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| 0.1518 | 8.0 | 4408 | 0.0517 | 0.9814 |
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| 0.1231 | 9.0 | 4959 | 0.0528 | 0.9812 |
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| 0.1076 | 10.0 | 5510 | 0.0426 | 0.9850 |
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| 0.0953 | 11.0 | 6061 | 0.0634 | 0.9795 |
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| 0.1097 | 12.0 | 6612 | 0.0398 | 0.9860 |
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| 0.0763 | 13.0 | 7163 | 0.0348 | 0.9866 |
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| 0.0895 | 14.0 | 7714 | 0.0341 | 0.9884 |
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| 0.06 | 15.0 | 8265 | 0.0381 | 0.9883 |
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| 0.0767 | 16.0 | 8816 | 0.0382 | 0.9875 |
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| 0.0868 | 17.0 | 9367 | 0.0309 | 0.9898 |
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| 0.091 | 18.0 | 9918 | 0.0339 | 0.9885 |
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| 0.0817 | 19.0 | 10469 | 0.0243 | 0.9913 |
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| 0.0641 | 20.0 | 11020 | 0.0286 | 0.9906 |
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| 0.0703 | 21.0 | 11571 | 0.0314 | 0.9906 |
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| 0.0642 | 22.0 | 12122 | 0.0261 | 0.9913 |
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| 0.0695 | 23.0 | 12673 | 0.0260 | 0.9920 |
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| 0.0664 | 24.0 | 13224 | 0.0241 | 0.9928 |
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| 0.0552 | 25.0 | 13775 | 0.0258 | 0.9928 |
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| 0.056 | 26.0 | 14326 | 0.0230 | 0.9939 |
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| 0.0488 | 27.0 | 14877 | 0.0221 | 0.9936 |
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| 0.0389 | 28.0 | 15428 | 0.0225 | 0.9930 |
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| 0.0402 | 29.0 | 15979 | 0.0231 | 0.9940 |
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| 0.0424 | 30.0 | 16530 | 0.0211 | 0.9939 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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