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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_e8 |
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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_e8 |
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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: 0.4608 |
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- Accuracy: 0.9052 |
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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: 8 |
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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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| 4.2202 | 0.08 | 100 | 4.1245 | 0.1237 | |
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| 3.467 | 0.16 | 200 | 3.5622 | 0.2143 | |
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| 3.3469 | 0.23 | 300 | 3.1688 | 0.2675 | |
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| 2.8086 | 0.31 | 400 | 2.8965 | 0.3034 | |
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| 2.6291 | 0.39 | 500 | 2.5858 | 0.4025 | |
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| 2.2382 | 0.47 | 600 | 2.2908 | 0.4133 | |
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| 1.9259 | 0.55 | 700 | 2.2007 | 0.4676 | |
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| 1.8088 | 0.63 | 800 | 2.0419 | 0.4742 | |
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| 1.9462 | 0.7 | 900 | 1.6793 | 0.5578 | |
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| 1.5392 | 0.78 | 1000 | 1.5460 | 0.6079 | |
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| 1.561 | 0.86 | 1100 | 1.5793 | 0.5690 | |
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| 1.2135 | 0.94 | 1200 | 1.4663 | 0.5929 | |
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| 1.0725 | 1.02 | 1300 | 1.2974 | 0.6534 | |
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| 0.8696 | 1.1 | 1400 | 1.2406 | 0.6569 | |
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| 0.8758 | 1.17 | 1500 | 1.2127 | 0.6623 | |
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| 1.1737 | 1.25 | 1600 | 1.2243 | 0.6550 | |
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| 0.8242 | 1.33 | 1700 | 1.1371 | 0.6735 | |
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| 1.0141 | 1.41 | 1800 | 1.0536 | 0.7024 | |
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| 0.9855 | 1.49 | 1900 | 0.9885 | 0.7205 | |
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| 0.805 | 1.57 | 2000 | 0.9048 | 0.7479 | |
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| 0.7207 | 1.64 | 2100 | 0.8842 | 0.7490 | |
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| 0.7101 | 1.72 | 2200 | 0.8954 | 0.7436 | |
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| 0.5946 | 1.8 | 2300 | 0.9174 | 0.7386 | |
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| 0.6937 | 1.88 | 2400 | 0.7818 | 0.7760 | |
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| 0.5593 | 1.96 | 2500 | 0.7449 | 0.7934 | |
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| 0.4139 | 2.04 | 2600 | 0.7787 | 0.7830 | |
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| 0.2929 | 2.11 | 2700 | 0.7122 | 0.7945 | |
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| 0.4159 | 2.19 | 2800 | 0.7446 | 0.7907 | |
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| 0.4079 | 2.27 | 2900 | 0.7354 | 0.7938 | |
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| 0.516 | 2.35 | 3000 | 0.7499 | 0.8007 | |
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| 0.2728 | 2.43 | 3100 | 0.6851 | 0.8061 | |
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| 0.4159 | 2.51 | 3200 | 0.7258 | 0.7999 | |
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| 0.3396 | 2.58 | 3300 | 0.7455 | 0.7972 | |
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| 0.1918 | 2.66 | 3400 | 0.6793 | 0.8119 | |
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| 0.1228 | 2.74 | 3500 | 0.6696 | 0.8134 | |
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| 0.2671 | 2.82 | 3600 | 0.6306 | 0.8285 | |
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| 0.4986 | 2.9 | 3700 | 0.6111 | 0.8296 | |
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| 0.3699 | 2.98 | 3800 | 0.5600 | 0.8508 | |
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| 0.0444 | 3.05 | 3900 | 0.6021 | 0.8331 | |
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| 0.1489 | 3.13 | 4000 | 0.5599 | 0.8516 | |
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| 0.15 | 3.21 | 4100 | 0.6377 | 0.8365 | |
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| 0.2535 | 3.29 | 4200 | 0.5752 | 0.8543 | |
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| 0.2679 | 3.37 | 4300 | 0.5677 | 0.8608 | |
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| 0.0989 | 3.45 | 4400 | 0.6325 | 0.8396 | |
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| 0.0825 | 3.52 | 4500 | 0.5979 | 0.8524 | |
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| 0.0427 | 3.6 | 4600 | 0.5903 | 0.8516 | |
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| 0.1806 | 3.68 | 4700 | 0.5323 | 0.8628 | |
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| 0.2672 | 3.76 | 4800 | 0.5688 | 0.8604 | |
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| 0.2674 | 3.84 | 4900 | 0.5369 | 0.8635 | |
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| 0.2185 | 3.92 | 5000 | 0.4743 | 0.8820 | |
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| 0.2195 | 3.99 | 5100 | 0.5340 | 0.8709 | |
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| 0.0049 | 4.07 | 5200 | 0.5883 | 0.8608 | |
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| 0.0204 | 4.15 | 5300 | 0.6102 | 0.8539 | |
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| 0.0652 | 4.23 | 5400 | 0.5659 | 0.8670 | |
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| 0.028 | 4.31 | 5500 | 0.4916 | 0.8840 | |
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| 0.0423 | 4.39 | 5600 | 0.5706 | 0.8736 | |
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| 0.0087 | 4.46 | 5700 | 0.5653 | 0.8697 | |
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| 0.0964 | 4.54 | 5800 | 0.5423 | 0.8755 | |
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| 0.0841 | 4.62 | 5900 | 0.5160 | 0.8743 | |
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| 0.0945 | 4.7 | 6000 | 0.5532 | 0.8697 | |
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| 0.0311 | 4.78 | 6100 | 0.4947 | 0.8867 | |
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| 0.0423 | 4.86 | 6200 | 0.5063 | 0.8843 | |
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| 0.1348 | 4.93 | 6300 | 0.5619 | 0.8743 | |
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| 0.049 | 5.01 | 6400 | 0.5800 | 0.8732 | |
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| 0.0053 | 5.09 | 6500 | 0.5499 | 0.8770 | |
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| 0.0234 | 5.17 | 6600 | 0.5102 | 0.8874 | |
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| 0.0192 | 5.25 | 6700 | 0.5447 | 0.8836 | |
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| 0.0029 | 5.32 | 6800 | 0.4787 | 0.8936 | |
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| 0.0249 | 5.4 | 6900 | 0.5232 | 0.8870 | |
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| 0.0671 | 5.48 | 7000 | 0.4766 | 0.8975 | |
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| 0.0056 | 5.56 | 7100 | 0.5136 | 0.8894 | |
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| 0.003 | 5.64 | 7200 | 0.5085 | 0.8882 | |
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| 0.0015 | 5.72 | 7300 | 0.4832 | 0.8971 | |
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| 0.0014 | 5.79 | 7400 | 0.4648 | 0.8998 | |
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| 0.0065 | 5.87 | 7500 | 0.4739 | 0.8978 | |
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| 0.0011 | 5.95 | 7600 | 0.5349 | 0.8867 | |
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| 0.0021 | 6.03 | 7700 | 0.5460 | 0.8847 | |
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| 0.0012 | 6.11 | 7800 | 0.5309 | 0.8890 | |
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| 0.0011 | 6.19 | 7900 | 0.4852 | 0.8998 | |
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| 0.0093 | 6.26 | 8000 | 0.4751 | 0.8998 | |
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| 0.003 | 6.34 | 8100 | 0.4934 | 0.8963 | |
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| 0.0027 | 6.42 | 8200 | 0.4882 | 0.9029 | |
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| 0.0009 | 6.5 | 8300 | 0.4806 | 0.9021 | |
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| 0.0009 | 6.58 | 8400 | 0.4974 | 0.9029 | |
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| 0.0009 | 6.66 | 8500 | 0.4748 | 0.9075 | |
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| 0.0008 | 6.73 | 8600 | 0.4723 | 0.9094 | |
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| 0.001 | 6.81 | 8700 | 0.4692 | 0.9098 | |
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| 0.0007 | 6.89 | 8800 | 0.4726 | 0.9075 | |
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| 0.0011 | 6.97 | 8900 | 0.4686 | 0.9067 | |
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| 0.0006 | 7.05 | 9000 | 0.4653 | 0.9056 | |
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| 0.0006 | 7.13 | 9100 | 0.4755 | 0.9029 | |
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| 0.0007 | 7.2 | 9200 | 0.4633 | 0.9036 | |
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| 0.0067 | 7.28 | 9300 | 0.4611 | 0.9036 | |
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| 0.0007 | 7.36 | 9400 | 0.4608 | 0.9052 | |
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| 0.0007 | 7.44 | 9500 | 0.4623 | 0.9044 | |
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| 0.0005 | 7.52 | 9600 | 0.4621 | 0.9056 | |
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| 0.0005 | 7.6 | 9700 | 0.4615 | 0.9056 | |
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| 0.0005 | 7.67 | 9800 | 0.4612 | 0.9059 | |
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| 0.0005 | 7.75 | 9900 | 0.4626 | 0.9075 | |
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| 0.0004 | 7.83 | 10000 | 0.4626 | 0.9075 | |
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| 0.0005 | 7.91 | 10100 | 0.4626 | 0.9075 | |
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| 0.0006 | 7.99 | 10200 | 0.4626 | 0.9079 | |
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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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