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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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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: vit-base-flowers102 |
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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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# vit-base-flowers102 |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the nelorth/oxford-flowers dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0770 |
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- Accuracy: 0.9853 |
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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: 0.0002 |
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- train_batch_size: 16 |
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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: 4 |
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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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| 2.5779 | 0.22 | 100 | 2.8895 | 0.7775 | |
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| 1.2226 | 0.45 | 200 | 1.5942 | 0.9255 | |
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| 0.606 | 0.67 | 300 | 0.8012 | 0.9529 | |
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| 0.3413 | 0.89 | 400 | 0.4845 | 0.9706 | |
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| 0.1571 | 1.11 | 500 | 0.2611 | 0.9814 | |
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| 0.1237 | 1.34 | 600 | 0.1691 | 0.9784 | |
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| 0.049 | 1.56 | 700 | 0.1146 | 0.9892 | |
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| 0.0763 | 1.78 | 800 | 0.1209 | 0.9863 | |
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| 0.0864 | 2.0 | 900 | 0.1223 | 0.9804 | |
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| 0.0786 | 2.23 | 1000 | 0.1075 | 0.9833 | |
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| 0.0269 | 2.45 | 1100 | 0.0919 | 0.9843 | |
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| 0.0178 | 2.67 | 1200 | 0.0795 | 0.9873 | |
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| 0.0165 | 2.9 | 1300 | 0.0727 | 0.9873 | |
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| 0.0144 | 3.12 | 1400 | 0.0784 | 0.9853 | |
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| 0.0138 | 3.34 | 1500 | 0.0759 | 0.9853 | |
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| 0.0135 | 3.56 | 1600 | 0.0737 | 0.9863 | |
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| 0.0123 | 3.79 | 1700 | 0.0770 | 0.9853 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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