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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- beans |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-mobilenet-beans-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: beans |
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type: beans |
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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: 0.7265625 |
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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 distilled to MobileNet |
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This model is a distilled model, where teacher model is [merve/beans-vit-224](https://huggingface.co/merve/beans-vit-224), fine-tuned [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. |
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Student model is randomly initialized MobileNetV2. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5922 |
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- Accuracy: 0.7266 |
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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: 8 |
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- eval_batch_size: 8 |
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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: 25 |
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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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| 0.9217 | 1.0 | 130 | 1.0079 | 0.3835 | |
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| 0.8973 | 2.0 | 260 | 0.8349 | 0.4286 | |
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| 0.7912 | 3.0 | 390 | 0.8905 | 0.5414 | |
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| 0.7151 | 4.0 | 520 | 1.1400 | 0.4887 | |
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| 0.6797 | 5.0 | 650 | 4.5343 | 0.4135 | |
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| 0.6471 | 6.0 | 780 | 2.1551 | 0.3985 | |
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| 0.5989 | 7.0 | 910 | 0.8552 | 0.6090 | |
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| 0.6252 | 8.0 | 1040 | 1.7453 | 0.5489 | |
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| 0.6025 | 9.0 | 1170 | 0.7852 | 0.6466 | |
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| 0.5643 | 10.0 | 1300 | 1.4728 | 0.6090 | |
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| 0.5505 | 11.0 | 1430 | 1.1570 | 0.6015 | |
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| 0.5207 | 12.0 | 1560 | 3.2526 | 0.4436 | |
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| 0.4957 | 13.0 | 1690 | 0.6617 | 0.6541 | |
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| 0.4935 | 14.0 | 1820 | 0.7502 | 0.6241 | |
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| 0.4836 | 15.0 | 1950 | 1.2039 | 0.5338 | |
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| 0.4648 | 16.0 | 2080 | 1.0283 | 0.5338 | |
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| 0.4662 | 17.0 | 2210 | 0.6695 | 0.7293 | |
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| 0.4351 | 18.0 | 2340 | 0.8694 | 0.5940 | |
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| 0.4286 | 19.0 | 2470 | 1.2751 | 0.4737 | |
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| 0.4166 | 20.0 | 2600 | 0.8719 | 0.6241 | |
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| 0.4263 | 21.0 | 2730 | 0.8767 | 0.6015 | |
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| 0.4261 | 22.0 | 2860 | 1.2780 | 0.5564 | |
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| 0.4124 | 23.0 | 2990 | 1.4095 | 0.5940 | |
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| 0.4082 | 24.0 | 3120 | 0.9104 | 0.6015 | |
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| 0.3923 | 25.0 | 3250 | 0.6430 | 0.7068 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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