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--- |
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license: other |
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base_model: apple/mobilevit-xx-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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model-index: |
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- name: quickdraw-MobileViT-xxs-a |
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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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# quickdraw-MobileViT-xxs-a |
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This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1512 |
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- Accuracy: 0.7126 |
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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: 512 |
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- eval_batch_size: 512 |
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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: cosine |
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- lr_scheduler_warmup_steps: 5000 |
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- num_epochs: 8 |
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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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| 1.8007 | 0.5688 | 5000 | 1.7490 | 0.5725 | |
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| 1.5116 | 1.1377 | 10000 | 1.5185 | 0.6256 | |
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| 1.4298 | 1.7065 | 15000 | 1.4384 | 0.6438 | |
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| 1.3622 | 2.2753 | 20000 | 1.3908 | 0.6547 | |
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| 1.332 | 2.8441 | 25000 | 1.3210 | 0.6712 | |
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| 1.2903 | 3.4130 | 30000 | 1.2758 | 0.6824 | |
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| 1.2693 | 3.9818 | 35000 | 1.2592 | 0.6864 | |
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| 1.2391 | 4.5506 | 40000 | 1.2169 | 0.6965 | |
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| 1.2078 | 5.1195 | 45000 | 1.1928 | 0.7023 | |
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| 1.1959 | 5.6883 | 50000 | 1.1779 | 0.7059 | |
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| 1.1749 | 6.2571 | 55000 | 1.1669 | 0.7083 | |
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| 1.1713 | 6.8259 | 60000 | 1.1564 | 0.7110 | |
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| 1.1573 | 7.3948 | 65000 | 1.1524 | 0.7123 | |
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| 1.1555 | 7.9636 | 70000 | 1.1512 | 0.7126 | |
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### Framework versions |
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- Transformers 4.41.0 |
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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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