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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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- image_folder
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metrics:
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- accuracy
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model-index:
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- name: violation-classification-bantai-vit-v80ep
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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: image_folder
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type: image_folder
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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: 0.9559725730783111
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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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# violation-classification-bantai-vit-v80ep
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the image_folder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1974
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- Accuracy: 0.9560
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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: 80
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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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| 0.797 | 4.95 | 500 | 0.3926 | 0.8715 |
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| 0.3095 | 9.9 | 1000 | 0.2597 | 0.9107 |
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| 0.1726 | 14.85 | 1500 | 0.2157 | 0.9253 |
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| 0.1259 | 19.8 | 2000 | 0.1870 | 0.9392 |
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| 0.0959 | 24.75 | 2500 | 0.1797 | 0.9444 |
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| 0.0835 | 29.7 | 3000 | 0.2293 | 0.9354 |
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| 0.0722 | 34.65 | 3500 | 0.1921 | 0.9441 |
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| 0.0628 | 39.6 | 4000 | 0.1897 | 0.9491 |
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| 0.059 | 44.55 | 4500 | 0.1719 | 0.9520 |
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| 0.0531 | 49.5 | 5000 | 0.1987 | 0.9513 |
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| 0.046 | 54.45 | 5500 | 0.1713 | 0.9556 |
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| 0.0444 | 59.4 | 6000 | 0.2016 | 0.9525 |
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| 0.042 | 64.36 | 6500 | 0.1950 | 0.9525 |
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| 0.0363 | 69.31 | 7000 | 0.2017 | 0.9549 |
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| 0.037 | 74.26 | 7500 | 0.1943 | 0.9551 |
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| 0.0343 | 79.21 | 8000 | 0.1974 | 0.9560 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.0+cu111
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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