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---
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license: apache-2.0
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base_model: google/vit-base-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: vit-base-patch16-224-ve-U13b-80RX1
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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: 0.10869565217391304
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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-ve-U13b-80RX1
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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: 25872499347325405328572416.0000
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- Accuracy: 0.1087
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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: 5.5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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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: 0.05
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- num_epochs: 40
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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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| 21407918734188223332876288.0000 | 1.0 | 103 | 25872499347325405328572416.0000 | 0.1087 |
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| 19230841377306649816989696.0000 | 2.0 | 206 | 25872499347325405328572416.0000 | 0.1087 |
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| 22859301179210058793222144.0000 | 3.0 | 309 | 25872499347325405328572416.0000 | 0.1087 |
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| 23584992401720978670878720.0000 | 4.0 | 412 | 25872499347325405328572416.0000 | 0.1087 |
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| 24310687313580712431452160.0000 | 5.0 | 515 | 25872499347325405328572416.0000 | 0.1087 |
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| 24310687313580712431452160.0000 | 6.0 | 618 | 25872499347325405328572416.0000 | 0.1087 |
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| 22496457412629005795852288.0000 | 7.0 | 721 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045071278258356452589568.0000 | 8.0 | 824 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045071278258356452589568.0000 | 9.0 | 927 | 25872499347325405328572416.0000 | 0.1087 |
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| 23343098402008016231596032.0000 | 10.0 | 1030 | 25872499347325405328572416.0000 | 0.1087 |
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| 23222148635139925673508864.0000 | 11.0 | 1133 | 25872499347325405328572416.0000 | 0.1087 |
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| 23222150479814334762450944.0000 | 12.0 | 1236 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407918734188223332876288.0000 | 13.0 | 1339 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407916889513814243934208.0000 | 14.0 | 1442 | 25872499347325405328572416.0000 | 0.1087 |
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| 21770764345443681124220928.0000 | 15.0 | 1545 | 25872499347325405328572416.0000 | 0.1087 |
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| 22496455567954601001877504.0000 | 16.0 | 1648 | 25872499347325405328572416.0000 | 0.1087 |
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| 22859303023884467882164224.0000 | 17.0 | 1751 | 25872499347325405328572416.0000 | 0.1087 |
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| 19593686988562107608334336.0000 | 18.0 | 1854 | 25872499347325405328572416.0000 | 0.1087 |
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| 22859304868558872676139008.0000 | 19.0 | 1957 | 25872499347325405328572416.0000 | 0.1087 |
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| 21528866656381904802021376.0000 | 20.0 | 2060 | 25872499347325405328572416.0000 | 0.1087 |
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| 17053764020425078448586752.0000 | 21.0 | 2163 | 25872499347325405328572416.0000 | 0.1087 |
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| 22133609956699138915565568.0000 | 22.0 | 2266 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045074967607170335506432.0000 | 23.0 | 2369 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407915044839405154992128.0000 | 24.0 | 2472 | 25872499347325405328572416.0000 | 0.1087 |
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| 21770762500769272035278848.0000 | 25.0 | 2575 | 25872499347325405328572416.0000 | 0.1087 |
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| 23947841702325254640107520.0000 | 26.0 | 2678 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045071278258356452589568.0000 | 27.0 | 2781 | 25872499347325405328572416.0000 | 0.1087 |
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| 21770762500769272035278848.0000 | 28.0 | 2884 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407918734188223332876288.0000 | 29.0 | 2987 | 25872499347325405328572416.0000 | 0.1087 |
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| 21528866656381904802021376.0000 | 30.0 | 3090 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045073122932761246564352.0000 | 31.0 | 3193 | 25872499347325405328572416.0000 | 0.1087 |
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| 23584994246395387759820800.0000 | 32.0 | 3296 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045069433583947363647488.0000 | 33.0 | 3399 | 25872499347325405328572416.0000 | 0.1087 |
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| 22859304868558872676139008.0000 | 34.0 | 3502 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407920578862628126851072.0000 | 35.0 | 3605 | 25872499347325405328572416.0000 | 0.1087 |
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| 21045074967607170335506432.0000 | 36.0 | 3708 | 25872499347325405328572416.0000 | 0.1087 |
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| 21770764345443681124220928.0000 | 37.0 | 3811 | 25872499347325405328572416.0000 | 0.1087 |
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| 22496457412629005795852288.0000 | 38.0 | 3914 | 25872499347325405328572416.0000 | 0.1087 |
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| 21407918734188223332876288.0000 | 39.0 | 4017 | 25872499347325405328572416.0000 | 0.1087 |
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| 23222148635139925673508864.0000 | 40.0 | 4120 | 25872499347325405328572416.0000 | 0.1087 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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