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--- |
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license: apache-2.0 |
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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: exper3_mesum5 |
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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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# exper3_mesum5 |
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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 sudo-s/herbier_mesuem5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6366 |
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- Accuracy: 0.8367 |
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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: 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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| 3.895 | 0.23 | 100 | 3.8276 | 0.1935 | |
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| 3.1174 | 0.47 | 200 | 3.1217 | 0.3107 | |
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| 2.6 | 0.7 | 300 | 2.5399 | 0.4207 | |
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| 2.256 | 0.93 | 400 | 2.1767 | 0.5160 | |
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| 1.5441 | 1.16 | 500 | 1.8086 | 0.5852 | |
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| 1.3834 | 1.4 | 600 | 1.5565 | 0.6325 | |
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| 1.1995 | 1.63 | 700 | 1.3339 | 0.6763 | |
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| 1.0845 | 1.86 | 800 | 1.3299 | 0.6533 | |
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| 0.6472 | 2.09 | 900 | 1.0679 | 0.7219 | |
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| 0.5948 | 2.33 | 1000 | 1.0286 | 0.7124 | |
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| 0.5565 | 2.56 | 1100 | 0.9595 | 0.7284 | |
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| 0.4879 | 2.79 | 1200 | 0.8915 | 0.7420 | |
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| 0.2816 | 3.02 | 1300 | 0.8159 | 0.7763 | |
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| 0.2412 | 3.26 | 1400 | 0.7766 | 0.7911 | |
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| 0.2015 | 3.49 | 1500 | 0.7850 | 0.7828 | |
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| 0.274 | 3.72 | 1600 | 0.7361 | 0.7935 | |
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| 0.1244 | 3.95 | 1700 | 0.7299 | 0.7911 | |
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| 0.0794 | 4.19 | 1800 | 0.7441 | 0.7846 | |
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| 0.0915 | 4.42 | 1900 | 0.7614 | 0.7941 | |
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| 0.0817 | 4.65 | 2000 | 0.7310 | 0.8012 | |
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| 0.0561 | 4.88 | 2100 | 0.7222 | 0.8065 | |
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| 0.0165 | 5.12 | 2200 | 0.7515 | 0.8059 | |
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| 0.0168 | 5.35 | 2300 | 0.6687 | 0.8213 | |
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| 0.0212 | 5.58 | 2400 | 0.6671 | 0.8249 | |
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| 0.0389 | 5.81 | 2500 | 0.6893 | 0.8278 | |
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| 0.0087 | 6.05 | 2600 | 0.6839 | 0.8260 | |
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| 0.0087 | 6.28 | 2700 | 0.6412 | 0.8320 | |
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| 0.0077 | 6.51 | 2800 | 0.6366 | 0.8367 | |
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| 0.0065 | 6.74 | 2900 | 0.6697 | 0.8272 | |
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| 0.0061 | 6.98 | 3000 | 0.6510 | 0.8349 | |
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| 0.0185 | 7.21 | 3100 | 0.6452 | 0.8367 | |
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| 0.0059 | 7.44 | 3200 | 0.6426 | 0.8379 | |
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| 0.0062 | 7.67 | 3300 | 0.6398 | 0.8379 | |
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| 0.0315 | 7.91 | 3400 | 0.6397 | 0.8385 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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