sudo-s/exper_batch_8_e8
0611
1---2license: apache-2.03tags:4- image-classification5- generated_from_trainer6metrics:7- accuracy8model-index:9- name: exper_batch_8_e810 results: []11---12 13<!-- This model card has been generated automatically according to the information the Trainer had access to. You14should probably proofread and complete it, then remove this comment. -->15 16# exper_batch_8_e817 18This 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.19It achieves the following results on the evaluation set:20- Loss: 0.460821- Accuracy: 0.905222 23## Model description24 25More information needed26 27## Intended uses & limitations28 29More information needed30 31## Training and evaluation data32 33More information needed34 35## Training procedure36 37### Training hyperparameters38 39The following hyperparameters were used during training:40- learning_rate: 0.000241- train_batch_size: 842- eval_batch_size: 843- seed: 4244- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0845- lr_scheduler_type: linear46- num_epochs: 847- mixed_precision_training: Apex, opt level O148 49### Training results50 51| Training Loss | Epoch | Step | Validation Loss | Accuracy |52|:-------------:|:-----:|:-----:|:---------------:|:--------:|53| 4.2202 | 0.08 | 100 | 4.1245 | 0.1237 |54| 3.467 | 0.16 | 200 | 3.5622 | 0.2143 |55| 3.3469 | 0.23 | 300 | 3.1688 | 0.2675 |56| 2.8086 | 0.31 | 400 | 2.8965 | 0.3034 |57| 2.6291 | 0.39 | 500 | 2.5858 | 0.4025 |58| 2.2382 | 0.47 | 600 | 2.2908 | 0.4133 |59| 1.9259 | 0.55 | 700 | 2.2007 | 0.4676 |60| 1.8088 | 0.63 | 800 | 2.0419 | 0.4742 |61| 1.9462 | 0.7 | 900 | 1.6793 | 0.5578 |62| 1.5392 | 0.78 | 1000 | 1.5460 | 0.6079 |63| 1.561 | 0.86 | 1100 | 1.5793 | 0.5690 |64| 1.2135 | 0.94 | 1200 | 1.4663 | 0.5929 |65| 1.0725 | 1.02 | 1300 | 1.2974 | 0.6534 |66| 0.8696 | 1.1 | 1400 | 1.2406 | 0.6569 |67| 0.8758 | 1.17 | 1500 | 1.2127 | 0.6623 |68| 1.1737 | 1.25 | 1600 | 1.2243 | 0.6550 |69| 0.8242 | 1.33 | 1700 | 1.1371 | 0.6735 |70| 1.0141 | 1.41 | 1800 | 1.0536 | 0.7024 |71| 0.9855 | 1.49 | 1900 | 0.9885 | 0.7205 |72| 0.805 | 1.57 | 2000 | 0.9048 | 0.7479 |73| 0.7207 | 1.64 | 2100 | 0.8842 | 0.7490 |74| 0.7101 | 1.72 | 2200 | 0.8954 | 0.7436 |75| 0.5946 | 1.8 | 2300 | 0.9174 | 0.7386 |76| 0.6937 | 1.88 | 2400 | 0.7818 | 0.7760 |77| 0.5593 | 1.96 | 2500 | 0.7449 | 0.7934 |78| 0.4139 | 2.04 | 2600 | 0.7787 | 0.7830 |79| 0.2929 | 2.11 | 2700 | 0.7122 | 0.7945 |80| 0.4159 | 2.19 | 2800 | 0.7446 | 0.7907 |81| 0.4079 | 2.27 | 2900 | 0.7354 | 0.7938 |82| 0.516 | 2.35 | 3000 | 0.7499 | 0.8007 |83| 0.2728 | 2.43 | 3100 | 0.6851 | 0.8061 |84| 0.4159 | 2.51 | 3200 | 0.7258 | 0.7999 |85| 0.3396 | 2.58 | 3300 | 0.7455 | 0.7972 |86| 0.1918 | 2.66 | 3400 | 0.6793 | 0.8119 |87| 0.1228 | 2.74 | 3500 | 0.6696 | 0.8134 |88| 0.2671 | 2.82 | 3600 | 0.6306 | 0.8285 |89| 0.4986 | 2.9 | 3700 | 0.6111 | 0.8296 |90| 0.3699 | 2.98 | 3800 | 0.5600 | 0.8508 |91| 0.0444 | 3.05 | 3900 | 0.6021 | 0.8331 |92| 0.1489 | 3.13 | 4000 | 0.5599 | 0.8516 |93| 0.15 | 3.21 | 4100 | 0.6377 | 0.8365 |94| 0.2535 | 3.29 | 4200 | 0.5752 | 0.8543 |95| 0.2679 | 3.37 | 4300 | 0.5677 | 0.8608 |96| 0.0989 | 3.45 | 4400 | 0.6325 | 0.8396 |97| 0.0825 | 3.52 | 4500 | 0.5979 | 0.8524 |98| 0.0427 | 3.6 | 4600 | 0.5903 | 0.8516 |99| 0.1806 | 3.68 | 4700 | 0.5323 | 0.8628 |100| 0.2672 | 3.76 | 4800 | 0.5688 | 0.8604 |101| 0.2674 | 3.84 | 4900 | 0.5369 | 0.8635 |102| 0.2185 | 3.92 | 5000 | 0.4743 | 0.8820 |103| 0.2195 | 3.99 | 5100 | 0.5340 | 0.8709 |104| 0.0049 | 4.07 | 5200 | 0.5883 | 0.8608 |105| 0.0204 | 4.15 | 5300 | 0.6102 | 0.8539 |106| 0.0652 | 4.23 | 5400 | 0.5659 | 0.8670 |107| 0.028 | 4.31 | 5500 | 0.4916 | 0.8840 |108| 0.0423 | 4.39 | 5600 | 0.5706 | 0.8736 |109| 0.0087 | 4.46 | 5700 | 0.5653 | 0.8697 |110| 0.0964 | 4.54 | 5800 | 0.5423 | 0.8755 |111| 0.0841 | 4.62 | 5900 | 0.5160 | 0.8743 |112| 0.0945 | 4.7 | 6000 | 0.5532 | 0.8697 |113| 0.0311 | 4.78 | 6100 | 0.4947 | 0.8867 |114| 0.0423 | 4.86 | 6200 | 0.5063 | 0.8843 |115| 0.1348 | 4.93 | 6300 | 0.5619 | 0.8743 |116| 0.049 | 5.01 | 6400 | 0.5800 | 0.8732 |117| 0.0053 | 5.09 | 6500 | 0.5499 | 0.8770 |118| 0.0234 | 5.17 | 6600 | 0.5102 | 0.8874 |119| 0.0192 | 5.25 | 6700 | 0.5447 | 0.8836 |120| 0.0029 | 5.32 | 6800 | 0.4787 | 0.8936 |121| 0.0249 | 5.4 | 6900 | 0.5232 | 0.8870 |122| 0.0671 | 5.48 | 7000 | 0.4766 | 0.8975 |123| 0.0056 | 5.56 | 7100 | 0.5136 | 0.8894 |124| 0.003 | 5.64 | 7200 | 0.5085 | 0.8882 |125| 0.0015 | 5.72 | 7300 | 0.4832 | 0.8971 |126| 0.0014 | 5.79 | 7400 | 0.4648 | 0.8998 |127| 0.0065 | 5.87 | 7500 | 0.4739 | 0.8978 |128| 0.0011 | 5.95 | 7600 | 0.5349 | 0.8867 |129| 0.0021 | 6.03 | 7700 | 0.5460 | 0.8847 |130| 0.0012 | 6.11 | 7800 | 0.5309 | 0.8890 |131| 0.0011 | 6.19 | 7900 | 0.4852 | 0.8998 |132| 0.0093 | 6.26 | 8000 | 0.4751 | 0.8998 |133| 0.003 | 6.34 | 8100 | 0.4934 | 0.8963 |134| 0.0027 | 6.42 | 8200 | 0.4882 | 0.9029 |135| 0.0009 | 6.5 | 8300 | 0.4806 | 0.9021 |136| 0.0009 | 6.58 | 8400 | 0.4974 | 0.9029 |137| 0.0009 | 6.66 | 8500 | 0.4748 | 0.9075 |138| 0.0008 | 6.73 | 8600 | 0.4723 | 0.9094 |139| 0.001 | 6.81 | 8700 | 0.4692 | 0.9098 |140| 0.0007 | 6.89 | 8800 | 0.4726 | 0.9075 |141| 0.0011 | 6.97 | 8900 | 0.4686 | 0.9067 |142| 0.0006 | 7.05 | 9000 | 0.4653 | 0.9056 |143| 0.0006 | 7.13 | 9100 | 0.4755 | 0.9029 |144| 0.0007 | 7.2 | 9200 | 0.4633 | 0.9036 |145| 0.0067 | 7.28 | 9300 | 0.4611 | 0.9036 |146| 0.0007 | 7.36 | 9400 | 0.4608 | 0.9052 |147| 0.0007 | 7.44 | 9500 | 0.4623 | 0.9044 |148| 0.0005 | 7.52 | 9600 | 0.4621 | 0.9056 |149| 0.0005 | 7.6 | 9700 | 0.4615 | 0.9056 |150| 0.0005 | 7.67 | 9800 | 0.4612 | 0.9059 |151| 0.0005 | 7.75 | 9900 | 0.4626 | 0.9075 |152| 0.0004 | 7.83 | 10000 | 0.4626 | 0.9075 |153| 0.0005 | 7.91 | 10100 | 0.4626 | 0.9075 |154| 0.0006 | 7.99 | 10200 | 0.4626 | 0.9079 |155 156 157### Framework versions158 159- Transformers 4.19.4160- Pytorch 1.5.1161- Datasets 2.3.2162- Tokenizers 0.12.1163