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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: vc-bantai-vit-withoutAMBI-adunest-v3 |
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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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args: Violation-Classification---Raw-10 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8218352310783658 |
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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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# vc-bantai-vit-withoutAMBI-adunest-v3 |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8889 |
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- Accuracy: 0.8218 |
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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: 0.0005 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 200 |
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- mixed_precision_training: Native AMP |
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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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| No log | 0.38 | 100 | 0.8208 | 0.7147 | |
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| No log | 0.76 | 200 | 0.8861 | 0.7595 | |
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| No log | 1.14 | 300 | 0.4306 | 0.7910 | |
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| No log | 1.52 | 400 | 0.5222 | 0.8245 | |
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| 0.3448 | 1.9 | 500 | 0.8621 | 0.7602 | |
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| 0.3448 | 2.28 | 600 | 0.2902 | 0.8801 | |
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| 0.3448 | 2.66 | 700 | 0.3687 | 0.8426 | |
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| 0.3448 | 3.04 | 800 | 0.3585 | 0.8694 | |
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| 0.3448 | 3.42 | 900 | 0.6546 | 0.7897 | |
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| 0.2183 | 3.8 | 1000 | 0.3881 | 0.8272 | |
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| 0.2183 | 4.18 | 1100 | 0.9650 | 0.7709 | |
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| 0.2183 | 4.56 | 1200 | 0.6444 | 0.7917 | |
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| 0.2183 | 4.94 | 1300 | 0.4685 | 0.8707 | |
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| 0.2183 | 5.32 | 1400 | 0.4972 | 0.8506 | |
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| 0.157 | 5.7 | 1500 | 0.4010 | 0.8513 | |
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| 0.157 | 6.08 | 1600 | 0.4629 | 0.8419 | |
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| 0.157 | 6.46 | 1700 | 0.4258 | 0.8714 | |
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| 0.157 | 6.84 | 1800 | 0.4383 | 0.8573 | |
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| 0.157 | 7.22 | 1900 | 0.5324 | 0.8493 | |
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| 0.113 | 7.6 | 2000 | 0.3212 | 0.8942 | |
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| 0.113 | 7.98 | 2100 | 0.8621 | 0.8326 | |
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| 0.113 | 8.37 | 2200 | 0.6050 | 0.8131 | |
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| 0.113 | 8.75 | 2300 | 0.7173 | 0.7991 | |
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| 0.113 | 9.13 | 2400 | 0.5313 | 0.8125 | |
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| 0.0921 | 9.51 | 2500 | 0.6584 | 0.8158 | |
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| 0.0921 | 9.89 | 2600 | 0.8727 | 0.7930 | |
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| 0.0921 | 10.27 | 2700 | 0.4222 | 0.8922 | |
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| 0.0921 | 10.65 | 2800 | 0.5811 | 0.8265 | |
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| 0.0921 | 11.03 | 2900 | 0.6175 | 0.8372 | |
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| 0.0701 | 11.41 | 3000 | 0.3914 | 0.8835 | |
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| 0.0701 | 11.79 | 3100 | 0.3364 | 0.8654 | |
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| 0.0701 | 12.17 | 3200 | 0.6223 | 0.8359 | |
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| 0.0701 | 12.55 | 3300 | 0.7830 | 0.8125 | |
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| 0.0701 | 12.93 | 3400 | 0.4356 | 0.8942 | |
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| 0.0552 | 13.31 | 3500 | 0.7553 | 0.8232 | |
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| 0.0552 | 13.69 | 3600 | 0.9107 | 0.8292 | |
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| 0.0552 | 14.07 | 3700 | 0.6108 | 0.8580 | |
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| 0.0552 | 14.45 | 3800 | 0.5732 | 0.8567 | |
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| 0.0552 | 14.83 | 3900 | 0.5087 | 0.8614 | |
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| 0.0482 | 15.21 | 4000 | 0.8889 | 0.8218 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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