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--- |
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license: other |
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base_model: apple/mobilevit-small |
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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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- f1 |
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- accuracy |
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model-index: |
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- name: car_identified_model_11 |
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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: train |
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args: default |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.7241379310344829 |
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- name: Accuracy |
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type: accuracy |
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value: 0.08333333333333333 |
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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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# car_identified_model_11 |
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This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co./apple/mobilevit-small) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6686 |
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- F1: 0.7241 |
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- Roc Auc: 0.6667 |
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- Accuracy: 0.0833 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.2582 | 1.0 | 1 | 0.6938 | 0.5926 | 0.5417 | 0.0833 | |
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| 0.2582 | 2.0 | 2 | 0.6937 | 0.6415 | 0.6042 | 0.0833 | |
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| 0.2582 | 3.0 | 4 | 0.6918 | 0.6429 | 0.5833 | 0.0 | |
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| 0.2582 | 4.0 | 5 | 0.6893 | 0.6316 | 0.5625 | 0.0 | |
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| 0.2582 | 5.0 | 6 | 0.6871 | 0.6667 | 0.6042 | 0.0833 | |
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| 0.2582 | 6.0 | 8 | 0.6844 | 0.6786 | 0.625 | 0.0833 | |
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| 0.2582 | 7.0 | 9 | 0.6827 | 0.7018 | 0.6458 | 0.0833 | |
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| 0.2582 | 8.0 | 10 | 0.6817 | 0.6667 | 0.6042 | 0.0833 | |
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| 0.2582 | 9.0 | 11 | 0.6809 | 0.6897 | 0.625 | 0.0833 | |
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| 0.2582 | 10.0 | 12 | 0.6804 | 0.6897 | 0.625 | 0.0833 | |
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| 0.2582 | 11.0 | 14 | 0.6792 | 0.6897 | 0.625 | 0.0833 | |
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| 0.2582 | 12.0 | 15 | 0.6787 | 0.7119 | 0.6458 | 0.0833 | |
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| 0.2582 | 13.0 | 16 | 0.6780 | 0.7119 | 0.6458 | 0.0833 | |
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| 0.2582 | 14.0 | 18 | 0.6771 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 15.0 | 19 | 0.6765 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 16.0 | 20 | 0.6762 | 0.7458 | 0.6875 | 0.0833 | |
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| 0.2582 | 17.0 | 21 | 0.6758 | 0.7333 | 0.6667 | 0.0833 | |
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| 0.2582 | 18.0 | 22 | 0.6753 | 0.7458 | 0.6875 | 0.0833 | |
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| 0.2582 | 19.0 | 24 | 0.6744 | 0.7333 | 0.6667 | 0.0833 | |
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| 0.2582 | 20.0 | 25 | 0.6740 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 21.0 | 26 | 0.6737 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 22.0 | 28 | 0.6733 | 0.7458 | 0.6875 | 0.0833 | |
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| 0.2582 | 23.0 | 29 | 0.6725 | 0.7458 | 0.6875 | 0.0833 | |
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| 0.2582 | 24.0 | 30 | 0.6720 | 0.7368 | 0.6875 | 0.0833 | |
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| 0.2582 | 25.0 | 31 | 0.6719 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 26.0 | 32 | 0.6713 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 27.0 | 34 | 0.6711 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 28.0 | 35 | 0.6705 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 29.0 | 36 | 0.6700 | 0.7368 | 0.6875 | 0.0833 | |
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| 0.2582 | 30.0 | 38 | 0.6696 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 31.0 | 39 | 0.6695 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 32.0 | 40 | 0.6693 | 0.7368 | 0.6875 | 0.1667 | |
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| 0.2582 | 33.0 | 41 | 0.6692 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 34.0 | 42 | 0.6694 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 35.0 | 44 | 0.6692 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 36.0 | 45 | 0.6693 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 37.0 | 46 | 0.6693 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 38.0 | 48 | 0.6690 | 0.7241 | 0.6667 | 0.0833 | |
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| 0.2582 | 39.0 | 49 | 0.6689 | 0.7368 | 0.6875 | 0.0833 | |
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| 0.2582 | 40.0 | 50 | 0.6686 | 0.7241 | 0.6667 | 0.0833 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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