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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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