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--- |
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tags: |
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- masked-image-modeling |
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- generated_from_trainer |
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datasets: |
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- cifar10 |
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model-index: |
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- name: vit-cifar10 |
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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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# ViT pre-trained from scratch on CIFAR10 |
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This model is a ViT (with the same arch as Google's [vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) pre-trained from scratch on the cifar10 dataset for masked image modeling. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0891 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 1337 |
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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: 100.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 0.289 | 1.0 | 2657 | 0.2941 | |
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| 0.2858 | 2.0 | 5314 | 0.2809 | |
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| 0.2693 | 3.0 | 7971 | 0.2738 | |
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| 0.2578 | 4.0 | 10628 | 0.2546 | |
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| 0.2211 | 5.0 | 13285 | 0.2153 | |
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| 0.1799 | 6.0 | 15942 | 0.1795 | |
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| 0.158 | 7.0 | 18599 | 0.1623 | |
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| 0.1481 | 8.0 | 21256 | 0.1453 | |
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| 0.1391 | 9.0 | 23913 | 0.1368 | |
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| 0.1348 | 10.0 | 26570 | 0.1354 | |
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| 0.129 | 11.0 | 29227 | 0.1249 | |
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| 0.126 | 12.0 | 31884 | 0.1229 | |
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| 0.1216 | 13.0 | 34541 | 0.1184 | |
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| 0.1175 | 14.0 | 37198 | 0.1185 | |
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| 0.1137 | 15.0 | 39855 | 0.1146 | |
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| 0.1125 | 16.0 | 42512 | 0.1117 | |
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| 0.1112 | 17.0 | 45169 | 0.1100 | |
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| 0.1108 | 18.0 | 47826 | 0.1089 | |
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| 0.1061 | 19.0 | 50483 | 0.1070 | |
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| 0.1073 | 20.0 | 53140 | 0.1076 | |
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| 0.1066 | 21.0 | 55797 | 0.1061 | |
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| 0.1065 | 22.0 | 58454 | 0.1056 | |
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| 0.1045 | 23.0 | 61111 | 0.1037 | |
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| 0.1052 | 24.0 | 63768 | 0.1055 | |
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| 0.102 | 25.0 | 66425 | 0.1028 | |
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| 0.1025 | 26.0 | 69082 | 0.1034 | |
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| 0.1037 | 27.0 | 71739 | 0.1025 | |
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| 0.1022 | 28.0 | 74396 | 0.1014 | |
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| 0.1026 | 29.0 | 77053 | 0.1011 | |
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| 0.1022 | 30.0 | 79710 | 0.1001 | |
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| 0.0997 | 31.0 | 82367 | 0.1007 | |
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| 0.0998 | 32.0 | 85024 | 0.1016 | |
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| 0.1019 | 33.0 | 87681 | 0.1008 | |
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| 0.0999 | 34.0 | 90338 | 0.1000 | |
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| 0.0998 | 35.0 | 92995 | 0.0993 | |
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| 0.0994 | 36.0 | 95652 | 0.0992 | |
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| 0.0966 | 37.0 | 98309 | 0.0991 | |
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| 0.0997 | 38.0 | 100966 | 0.0970 | |
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| 0.0991 | 39.0 | 103623 | 0.0979 | |
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| 0.099 | 40.0 | 106280 | 0.0983 | |
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| 0.0974 | 41.0 | 108937 | 0.0980 | |
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| 0.0974 | 42.0 | 111594 | 0.0971 | |
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| 0.0972 | 43.0 | 114251 | 0.0970 | |
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| 0.0991 | 44.0 | 116908 | 0.0970 | |
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| 0.0979 | 45.0 | 119565 | 0.0972 | |
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| 0.097 | 46.0 | 122222 | 0.0970 | |
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| 0.0936 | 47.0 | 124879 | 0.0967 | |
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| 0.0948 | 48.0 | 127536 | 0.0967 | |
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| 0.0974 | 49.0 | 130193 | 0.0954 | |
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| 0.0958 | 50.0 | 132850 | 0.0954 | |
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| 0.0948 | 51.0 | 135507 | 0.0955 | |
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| 0.095 | 52.0 | 138164 | 0.0953 | |
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| 0.0939 | 53.0 | 140821 | 0.0945 | |
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| 0.0961 | 54.0 | 143478 | 0.0948 | |
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| 0.0964 | 55.0 | 146135 | 0.0955 | |
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| 0.0934 | 56.0 | 148792 | 0.0948 | |
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| 0.0965 | 57.0 | 151449 | 0.0943 | |
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| 0.0966 | 58.0 | 154106 | 0.0941 | |
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| 0.0926 | 59.0 | 156763 | 0.0938 | |
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| 0.0928 | 60.0 | 159420 | 0.0942 | |
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| 0.093 | 61.0 | 162077 | 0.0936 | |
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| 0.0939 | 62.0 | 164734 | 0.0939 | |
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| 0.0936 | 63.0 | 167391 | 0.0936 | |
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| 0.093 | 64.0 | 170048 | 0.0929 | |
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| 0.0929 | 65.0 | 172705 | 0.0930 | |
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| 0.0917 | 66.0 | 175362 | 0.0925 | |
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| 0.0948 | 67.0 | 178019 | 0.0932 | |
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| 0.0931 | 68.0 | 180676 | 0.0927 | |
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| 0.0911 | 69.0 | 183333 | 0.0922 | |
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| 0.0923 | 70.0 | 185990 | 0.0924 | |
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| 0.0923 | 71.0 | 188647 | 0.0923 | |
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| 0.0929 | 72.0 | 191304 | 0.0919 | |
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| 0.0916 | 73.0 | 193961 | 0.0923 | |
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| 0.0927 | 74.0 | 196618 | 0.0921 | |
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| 0.0907 | 75.0 | 199275 | 0.0922 | |
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| 0.0927 | 76.0 | 201932 | 0.0919 | |
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| 0.0925 | 77.0 | 204589 | 0.0913 | |
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| 0.0921 | 78.0 | 207246 | 0.0917 | |
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| 0.0895 | 79.0 | 209903 | 0.0912 | |
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| 0.0916 | 80.0 | 212560 | 0.0914 | |
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| 0.09 | 81.0 | 215217 | 0.0909 | |
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| 0.0916 | 82.0 | 217874 | 0.0908 | |
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| 0.0902 | 83.0 | 220531 | 0.0907 | |
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| 0.0911 | 84.0 | 223188 | 0.0910 | |
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| 0.091 | 85.0 | 225845 | 0.0903 | |
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| 0.0903 | 86.0 | 228502 | 0.0905 | |
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| 0.0907 | 87.0 | 231159 | 0.0901 | |
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| 0.0908 | 88.0 | 233816 | 0.0907 | |
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| 0.0911 | 89.0 | 236473 | 0.0902 | |
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| 0.0905 | 90.0 | 239130 | 0.0906 | |
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| 0.089 | 91.0 | 241787 | 0.0901 | |
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| 0.0908 | 92.0 | 244444 | 0.0896 | |
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| 0.0894 | 93.0 | 247101 | 0.0892 | |
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| 0.0899 | 94.0 | 249758 | 0.0893 | |
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| 0.0899 | 95.0 | 252415 | 0.0897 | |
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| 0.0904 | 96.0 | 255072 | 0.0898 | |
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| 0.0906 | 97.0 | 257729 | 0.0894 | |
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| 0.0892 | 98.0 | 260386 | 0.0894 | |
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| 0.0881 | 99.0 | 263043 | 0.0892 | |
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| 0.09 | 100.0 | 265700 | 0.0894 | |
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### Framework versions |
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- Transformers 4.19.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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