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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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- precision |
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- recall |
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model-index: |
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- name: vit-base-patch16-224 |
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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: 0.78 |
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- name: Precision |
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type: precision |
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value: 0.781535758027584 |
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- name: Recall |
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type: recall |
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value: 0.78 |
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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 |
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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: 0.4819 |
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- Accuracy: 0.78 |
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- Precision: 0.7815 |
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- Recall: 0.78 |
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- F1 Score: 0.7807 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| No log | 1.0 | 4 | 0.5936 | 0.7292 | 0.8028 | 0.7292 | 0.6191 | |
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| No log | 2.0 | 8 | 0.5702 | 0.7208 | 0.6468 | 0.7208 | 0.6283 | |
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| No log | 3.0 | 12 | 0.5834 | 0.7125 | 0.6933 | 0.7125 | 0.7000 | |
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| No log | 4.0 | 16 | 0.5471 | 0.7375 | 0.7034 | 0.7375 | 0.6846 | |
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| No log | 5.0 | 20 | 0.5487 | 0.725 | 0.6938 | 0.725 | 0.6982 | |
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| No log | 6.0 | 24 | 0.5253 | 0.7458 | 0.7182 | 0.7458 | 0.7116 | |
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| No log | 7.0 | 28 | 0.5556 | 0.7417 | 0.7393 | 0.7417 | 0.7404 | |
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| 0.5648 | 8.0 | 32 | 0.5183 | 0.7417 | 0.7155 | 0.7417 | 0.7165 | |
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| 0.5648 | 9.0 | 36 | 0.5159 | 0.7667 | 0.7504 | 0.7667 | 0.7522 | |
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| 0.5648 | 10.0 | 40 | 0.5137 | 0.7708 | 0.7579 | 0.7708 | 0.7609 | |
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| 0.5648 | 11.0 | 44 | 0.5014 | 0.7833 | 0.7693 | 0.7833 | 0.7643 | |
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| 0.5648 | 12.0 | 48 | 0.5157 | 0.75 | 0.7524 | 0.75 | 0.7511 | |
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| 0.5648 | 13.0 | 52 | 0.5151 | 0.7417 | 0.7441 | 0.7417 | 0.7428 | |
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| 0.5648 | 14.0 | 56 | 0.4908 | 0.7792 | 0.7653 | 0.7792 | 0.7663 | |
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| 0.3814 | 15.0 | 60 | 0.4901 | 0.7833 | 0.7723 | 0.7833 | 0.7747 | |
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| 0.3814 | 16.0 | 64 | 0.4993 | 0.7667 | 0.7689 | 0.7667 | 0.7677 | |
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| 0.3814 | 17.0 | 68 | 0.4814 | 0.7792 | 0.7642 | 0.7792 | 0.7627 | |
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| 0.3814 | 18.0 | 72 | 0.5165 | 0.7583 | 0.7796 | 0.7583 | 0.7656 | |
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| 0.3814 | 19.0 | 76 | 0.4817 | 0.7958 | 0.7915 | 0.7958 | 0.7933 | |
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| 0.3814 | 20.0 | 80 | 0.4748 | 0.8083 | 0.8036 | 0.8083 | 0.8054 | |
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| 0.3814 | 21.0 | 84 | 0.4831 | 0.8042 | 0.8033 | 0.8042 | 0.8037 | |
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| 0.3814 | 22.0 | 88 | 0.4795 | 0.8083 | 0.8013 | 0.8083 | 0.8032 | |
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| 0.2354 | 23.0 | 92 | 0.5048 | 0.7708 | 0.7790 | 0.7708 | 0.7743 | |
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| 0.2354 | 24.0 | 96 | 0.4838 | 0.8042 | 0.7974 | 0.8042 | 0.7995 | |
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| 0.2354 | 25.0 | 100 | 0.4894 | 0.7833 | 0.7833 | 0.7833 | 0.7833 | |
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| 0.2354 | 26.0 | 104 | 0.4852 | 0.8 | 0.7914 | 0.8 | 0.7933 | |
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| 0.2354 | 27.0 | 108 | 0.4882 | 0.8 | 0.7982 | 0.8 | 0.7990 | |
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| 0.2354 | 28.0 | 112 | 0.4932 | 0.7875 | 0.7929 | 0.7875 | 0.7898 | |
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| 0.2354 | 29.0 | 116 | 0.4883 | 0.8083 | 0.8036 | 0.8083 | 0.8054 | |
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| 0.1479 | 30.0 | 120 | 0.4886 | 0.8042 | 0.7974 | 0.8042 | 0.7995 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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