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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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metrics: |
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- accuracy |
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- f1 |
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- recall |
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model-index: |
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- name: test1 |
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results: [] |
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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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# test1 |
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This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1366 |
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- Accuracy: 0.7952 |
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- F1: 0.7855 |
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- Recall: 0.7952 |
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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: 0.0008 |
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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: 10 |
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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 | F1 | Recall | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:------:| |
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| No log | 0.4554 | 500 | 0.6376 | 0.7896 | 0.7429 | 0.7896 | |
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| 0.657 | 0.9107 | 1000 | 0.5839 | 0.8109 | 0.7679 | 0.8109 | |
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| 0.657 | 1.3661 | 1500 | 0.7632 | 0.7322 | 0.7195 | 0.7322 | |
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| 0.5653 | 1.8215 | 2000 | 0.5927 | 0.8144 | 0.7689 | 0.8144 | |
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| 0.5653 | 2.2769 | 2500 | 0.5855 | 0.8174 | 0.7765 | 0.8174 | |
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| 0.5128 | 2.7322 | 3000 | 0.5567 | 0.8210 | 0.7931 | 0.8210 | |
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| 0.5128 | 3.1876 | 3500 | 0.5578 | 0.8214 | 0.7894 | 0.8214 | |
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| 0.4648 | 3.6430 | 4000 | 0.5699 | 0.8236 | 0.7928 | 0.8236 | |
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| 0.4648 | 4.0984 | 4500 | 0.6039 | 0.8053 | 0.7850 | 0.8053 | |
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| 0.411 | 4.5537 | 5000 | 0.5662 | 0.8203 | 0.7989 | 0.8203 | |
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| 0.411 | 5.0091 | 5500 | 0.6043 | 0.8252 | 0.7962 | 0.8252 | |
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| 0.3532 | 5.4645 | 6000 | 0.6559 | 0.8060 | 0.7915 | 0.8060 | |
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| 0.3532 | 5.9199 | 6500 | 0.6310 | 0.8175 | 0.7919 | 0.8175 | |
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| 0.2847 | 6.3752 | 7000 | 0.7075 | 0.8029 | 0.7890 | 0.8029 | |
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| 0.2847 | 6.8306 | 7500 | 0.8056 | 0.7743 | 0.7745 | 0.7743 | |
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| 0.2265 | 7.2860 | 8000 | 0.8991 | 0.7957 | 0.7875 | 0.7957 | |
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| 0.2265 | 7.7413 | 8500 | 0.8929 | 0.7904 | 0.7866 | 0.7904 | |
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| 0.1626 | 8.1967 | 9000 | 0.9503 | 0.8022 | 0.7883 | 0.8022 | |
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| 0.1626 | 8.6521 | 9500 | 1.0467 | 0.7904 | 0.7838 | 0.7904 | |
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| 0.1099 | 9.1075 | 10000 | 1.0435 | 0.8009 | 0.7877 | 0.8009 | |
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| 0.1099 | 9.5628 | 10500 | 1.1366 | 0.7952 | 0.7855 | 0.7952 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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