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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-U8-40d
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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: 0.8431372549019608
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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-U8-40d
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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: 0.6495
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- Accuracy: 0.8431
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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: 6e-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: 0.05
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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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| 1.3419 | 1.0 | 20 | 1.2998 | 0.4706 |
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| 1.1313 | 2.0 | 40 | 1.0832 | 0.5686 |
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| 0.7969 | 3.0 | 60 | 0.8094 | 0.6667 |
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| 0.5063 | 4.0 | 80 | 0.6573 | 0.7843 |
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| 0.3367 | 5.0 | 100 | 0.6389 | 0.7647 |
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| 0.242 | 6.0 | 120 | 0.6879 | 0.7451 |
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| 0.1881 | 7.0 | 140 | 0.7940 | 0.7059 |
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| 0.1561 | 8.0 | 160 | 0.8030 | 0.7647 |
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| 0.1557 | 9.0 | 180 | 0.7004 | 0.8235 |
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| 0.1154 | 10.0 | 200 | 0.6495 | 0.8431 |
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| 0.1469 | 11.0 | 220 | 1.1388 | 0.7059 |
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| 0.0898 | 12.0 | 240 | 0.7967 | 0.7647 |
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| 0.0719 | 13.0 | 260 | 0.8934 | 0.8039 |
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| 0.0739 | 14.0 | 280 | 0.8476 | 0.7647 |
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| 0.0823 | 15.0 | 300 | 0.9692 | 0.7647 |
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| 0.0828 | 16.0 | 320 | 0.9385 | 0.7843 |
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| 0.0761 | 17.0 | 340 | 1.1684 | 0.7255 |
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| 0.0597 | 18.0 | 360 | 0.9414 | 0.7647 |
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| 0.0727 | 19.0 | 380 | 1.0201 | 0.7059 |
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| 0.0507 | 20.0 | 400 | 0.8563 | 0.8039 |
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| 0.0587 | 21.0 | 420 | 0.8476 | 0.7843 |
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| 0.0608 | 22.0 | 440 | 0.9399 | 0.8039 |
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| 0.055 | 23.0 | 460 | 0.8820 | 0.7451 |
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| 0.0619 | 24.0 | 480 | 1.0460 | 0.7647 |
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| 0.0615 | 25.0 | 500 | 0.9392 | 0.8235 |
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| 0.0455 | 26.0 | 520 | 0.9267 | 0.8235 |
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| 0.0567 | 27.0 | 540 | 0.9784 | 0.7843 |
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| 0.032 | 28.0 | 560 | 1.1541 | 0.7647 |
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| 0.0276 | 29.0 | 580 | 0.8865 | 0.7843 |
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| 0.0368 | 30.0 | 600 | 1.0848 | 0.8039 |
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| 0.0342 | 31.0 | 620 | 0.9638 | 0.8039 |
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| 0.037 | 32.0 | 640 | 0.9616 | 0.8039 |
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| 0.0371 | 33.0 | 660 | 1.0073 | 0.8039 |
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| 0.0371 | 34.0 | 680 | 1.0494 | 0.8039 |
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| 0.0359 | 35.0 | 700 | 1.1287 | 0.7843 |
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| 0.0255 | 36.0 | 720 | 1.1831 | 0.7647 |
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| 0.0269 | 37.0 | 740 | 1.1610 | 0.7843 |
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| 0.0292 | 38.0 | 760 | 1.1842 | 0.7843 |
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| 0.0161 | 39.0 | 780 | 1.1092 | 0.8039 |
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| 0.0333 | 40.0 | 800 | 1.1186 | 0.8039 |
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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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