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--- |
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license: other |
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base_model: apple/mobilevitv2-1.0-imagenet1k-256 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- food101 |
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metrics: |
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- accuracy |
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model-index: |
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- name: mobilevit-finetuned-food101 |
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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: food101 |
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type: food101 |
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config: default |
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split: train[:5000] |
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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: 0.874 |
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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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# mobilevit-finetuned-food101 |
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This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4191 |
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- Accuracy: 0.874 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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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- 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.9487 | 0.98 | 23 | 1.9476 | 0.151 | |
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| 1.9273 | 2.0 | 47 | 1.9070 | 0.24 | |
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| 1.8561 | 2.98 | 70 | 1.8401 | 0.448 | |
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| 1.7788 | 4.0 | 94 | 1.7301 | 0.612 | |
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| 1.6586 | 4.98 | 117 | 1.5863 | 0.676 | |
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| 1.4603 | 6.0 | 141 | 1.4199 | 0.72 | |
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| 1.3027 | 6.98 | 164 | 1.2215 | 0.734 | |
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| 1.1717 | 8.0 | 188 | 1.0581 | 0.759 | |
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| 0.9601 | 8.98 | 211 | 0.9013 | 0.769 | |
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| 0.8482 | 10.0 | 235 | 0.7866 | 0.791 | |
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| 0.7276 | 10.98 | 258 | 0.7112 | 0.803 | |
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| 0.6449 | 12.0 | 282 | 0.6132 | 0.835 | |
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| 0.6279 | 12.98 | 305 | 0.6069 | 0.83 | |
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| 0.5982 | 14.0 | 329 | 0.5637 | 0.832 | |
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| 0.5766 | 14.98 | 352 | 0.5149 | 0.857 | |
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| 0.5345 | 16.0 | 376 | 0.5392 | 0.837 | |
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| 0.494 | 16.98 | 399 | 0.5017 | 0.848 | |
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| 0.4953 | 18.0 | 423 | 0.5002 | 0.846 | |
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| 0.5118 | 18.98 | 446 | 0.4782 | 0.856 | |
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| 0.4708 | 20.0 | 470 | 0.4898 | 0.858 | |
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| 0.4774 | 20.98 | 493 | 0.4769 | 0.851 | |
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| 0.4848 | 22.0 | 517 | 0.4665 | 0.841 | |
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| 0.4533 | 22.98 | 540 | 0.4890 | 0.837 | |
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| 0.4449 | 24.0 | 564 | 0.4558 | 0.857 | |
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| 0.4205 | 24.98 | 587 | 0.4767 | 0.857 | |
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| 0.4417 | 26.0 | 611 | 0.4476 | 0.853 | |
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| 0.4333 | 26.98 | 634 | 0.4853 | 0.834 | |
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| 0.4545 | 28.0 | 658 | 0.4573 | 0.847 | |
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| 0.4489 | 28.98 | 681 | 0.4659 | 0.845 | |
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| 0.4172 | 29.36 | 690 | 0.4191 | 0.874 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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