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exper_batch_8_e4
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3353
- Accuracy: 0.9183
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Apex, opt level O1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.2251 | 0.08 | 100 | 4.1508 | 0.1203 |
| 3.4942 | 0.16 | 200 | 3.5566 | 0.2082 |
| 3.2871 | 0.23 | 300 | 3.0942 | 0.3092 |
| 2.7273 | 0.31 | 400 | 2.8338 | 0.3308 |
| 2.4984 | 0.39 | 500 | 2.4860 | 0.4341 |
| 2.3423 | 0.47 | 600 | 2.2201 | 0.4796 |
| 1.8785 | 0.55 | 700 | 2.1890 | 0.4653 |
| 1.8012 | 0.63 | 800 | 1.9901 | 0.4865 |
| 1.7236 | 0.7 | 900 | 1.6821 | 0.5736 |
| 1.4949 | 0.78 | 1000 | 1.5422 | 0.6083 |
| 1.5573 | 0.86 | 1100 | 1.5436 | 0.6110 |
| 1.3241 | 0.94 | 1200 | 1.4077 | 0.6207 |
| 1.0773 | 1.02 | 1300 | 1.1417 | 0.6916 |
| 0.7935 | 1.1 | 1400 | 1.1194 | 0.6931 |
| 0.7677 | 1.17 | 1500 | 1.0727 | 0.7167 |
| 0.9468 | 1.25 | 1600 | 1.0707 | 0.7136 |
| 0.7563 | 1.33 | 1700 | 0.9427 | 0.7390 |
| 0.8471 | 1.41 | 1800 | 0.8906 | 0.7571 |
| 0.9998 | 1.49 | 1900 | 0.8098 | 0.7845 |
| 0.6039 | 1.57 | 2000 | 0.7244 | 0.8034 |
| 0.7052 | 1.64 | 2100 | 0.7881 | 0.7953 |
| 0.6753 | 1.72 | 2200 | 0.7458 | 0.7926 |
| 0.3758 | 1.8 | 2300 | 0.6987 | 0.8022 |
| 0.4985 | 1.88 | 2400 | 0.6286 | 0.8265 |
| 0.4122 | 1.96 | 2500 | 0.5949 | 0.8358 |
| 0.1286 | 2.04 | 2600 | 0.5691 | 0.8385 |
| 0.1989 | 2.11 | 2700 | 0.5535 | 0.8389 |
| 0.3304 | 2.19 | 2800 | 0.5261 | 0.8520 |
| 0.3415 | 2.27 | 2900 | 0.5504 | 0.8477 |
| 0.4066 | 2.35 | 3000 | 0.5418 | 0.8497 |
| 0.1208 | 2.43 | 3100 | 0.5156 | 0.8612 |
| 0.1668 | 2.51 | 3200 | 0.5655 | 0.8539 |
| 0.0727 | 2.58 | 3300 | 0.4971 | 0.8658 |
| 0.0929 | 2.66 | 3400 | 0.4962 | 0.8635 |
| 0.0678 | 2.74 | 3500 | 0.4903 | 0.8670 |
| 0.1212 | 2.82 | 3600 | 0.4357 | 0.8867 |
| 0.1579 | 2.9 | 3700 | 0.4642 | 0.8739 |
| 0.2625 | 2.98 | 3800 | 0.3994 | 0.8951 |
| 0.024 | 3.05 | 3900 | 0.3953 | 0.8971 |
| 0.0696 | 3.13 | 4000 | 0.3883 | 0.9056 |
| 0.0169 | 3.21 | 4100 | 0.3755 | 0.9086 |
| 0.023 | 3.29 | 4200 | 0.3685 | 0.9109 |
| 0.0337 | 3.37 | 4300 | 0.3623 | 0.9109 |
| 0.0123 | 3.45 | 4400 | 0.3647 | 0.9067 |
| 0.0159 | 3.52 | 4500 | 0.3630 | 0.9082 |
| 0.0154 | 3.6 | 4600 | 0.3522 | 0.9094 |
| 0.0112 | 3.68 | 4700 | 0.3439 | 0.9163 |
| 0.0219 | 3.76 | 4800 | 0.3404 | 0.9194 |
| 0.0183 | 3.84 | 4900 | 0.3371 | 0.9183 |
| 0.0103 | 3.92 | 5000 | 0.3362 | 0.9183 |
| 0.0357 | 3.99 | 5100 | 0.3353 | 0.9183 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
