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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: exper_batch_8_e4 |
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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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# exper_batch_8_e4 |
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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_mesuem1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3353 |
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- Accuracy: 0.9183 |
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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: 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: 4 |
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- mixed_precision_training: Apex, opt level O1 |
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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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| 4.2251 | 0.08 | 100 | 4.1508 | 0.1203 | |
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| 3.4942 | 0.16 | 200 | 3.5566 | 0.2082 | |
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| 3.2871 | 0.23 | 300 | 3.0942 | 0.3092 | |
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| 2.7273 | 0.31 | 400 | 2.8338 | 0.3308 | |
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| 2.4984 | 0.39 | 500 | 2.4860 | 0.4341 | |
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| 2.3423 | 0.47 | 600 | 2.2201 | 0.4796 | |
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| 1.8785 | 0.55 | 700 | 2.1890 | 0.4653 | |
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| 1.8012 | 0.63 | 800 | 1.9901 | 0.4865 | |
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| 1.7236 | 0.7 | 900 | 1.6821 | 0.5736 | |
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| 1.4949 | 0.78 | 1000 | 1.5422 | 0.6083 | |
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| 1.5573 | 0.86 | 1100 | 1.5436 | 0.6110 | |
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| 1.3241 | 0.94 | 1200 | 1.4077 | 0.6207 | |
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| 1.0773 | 1.02 | 1300 | 1.1417 | 0.6916 | |
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| 0.7935 | 1.1 | 1400 | 1.1194 | 0.6931 | |
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| 0.7677 | 1.17 | 1500 | 1.0727 | 0.7167 | |
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| 0.9468 | 1.25 | 1600 | 1.0707 | 0.7136 | |
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| 0.7563 | 1.33 | 1700 | 0.9427 | 0.7390 | |
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| 0.8471 | 1.41 | 1800 | 0.8906 | 0.7571 | |
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| 0.9998 | 1.49 | 1900 | 0.8098 | 0.7845 | |
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| 0.6039 | 1.57 | 2000 | 0.7244 | 0.8034 | |
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| 0.7052 | 1.64 | 2100 | 0.7881 | 0.7953 | |
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| 0.6753 | 1.72 | 2200 | 0.7458 | 0.7926 | |
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| 0.3758 | 1.8 | 2300 | 0.6987 | 0.8022 | |
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| 0.4985 | 1.88 | 2400 | 0.6286 | 0.8265 | |
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| 0.4122 | 1.96 | 2500 | 0.5949 | 0.8358 | |
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| 0.1286 | 2.04 | 2600 | 0.5691 | 0.8385 | |
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| 0.1989 | 2.11 | 2700 | 0.5535 | 0.8389 | |
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| 0.3304 | 2.19 | 2800 | 0.5261 | 0.8520 | |
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| 0.3415 | 2.27 | 2900 | 0.5504 | 0.8477 | |
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| 0.4066 | 2.35 | 3000 | 0.5418 | 0.8497 | |
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| 0.1208 | 2.43 | 3100 | 0.5156 | 0.8612 | |
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| 0.1668 | 2.51 | 3200 | 0.5655 | 0.8539 | |
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| 0.0727 | 2.58 | 3300 | 0.4971 | 0.8658 | |
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| 0.0929 | 2.66 | 3400 | 0.4962 | 0.8635 | |
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| 0.0678 | 2.74 | 3500 | 0.4903 | 0.8670 | |
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| 0.1212 | 2.82 | 3600 | 0.4357 | 0.8867 | |
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| 0.1579 | 2.9 | 3700 | 0.4642 | 0.8739 | |
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| 0.2625 | 2.98 | 3800 | 0.3994 | 0.8951 | |
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| 0.024 | 3.05 | 3900 | 0.3953 | 0.8971 | |
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| 0.0696 | 3.13 | 4000 | 0.3883 | 0.9056 | |
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| 0.0169 | 3.21 | 4100 | 0.3755 | 0.9086 | |
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| 0.023 | 3.29 | 4200 | 0.3685 | 0.9109 | |
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| 0.0337 | 3.37 | 4300 | 0.3623 | 0.9109 | |
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| 0.0123 | 3.45 | 4400 | 0.3647 | 0.9067 | |
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| 0.0159 | 3.52 | 4500 | 0.3630 | 0.9082 | |
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| 0.0154 | 3.6 | 4600 | 0.3522 | 0.9094 | |
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| 0.0112 | 3.68 | 4700 | 0.3439 | 0.9163 | |
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| 0.0219 | 3.76 | 4800 | 0.3404 | 0.9194 | |
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| 0.0183 | 3.84 | 4900 | 0.3371 | 0.9183 | |
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| 0.0103 | 3.92 | 5000 | 0.3362 | 0.9183 | |
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| 0.0357 | 3.99 | 5100 | 0.3353 | 0.9183 | |
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### Framework versions |
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- Transformers 4.19.4 |
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- Pytorch 1.5.1 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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