--- license: apache-2.0 base_model: google/vit-base-patch32-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: FASHION-vision results: [] --- # FASHION-vision This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3997 - Accuracy: 0.9067 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 170 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.8832 | 1.0 | 375 | 1.8439 | 0.6338 | | 1.0135 | 2.0 | 750 | 1.0339 | 0.7624 | | 0.7743 | 3.0 | 1125 | 0.7679 | 0.8057 | | 0.6289 | 4.0 | 1500 | 0.6305 | 0.8285 | | 0.5332 | 5.0 | 1875 | 0.5465 | 0.8438 | | 0.4558 | 6.0 | 2250 | 0.5083 | 0.8458 | | 0.4344 | 7.0 | 2625 | 0.4553 | 0.8518 | | 0.4183 | 8.0 | 3000 | 0.4187 | 0.8623 | | 0.3838 | 9.0 | 3375 | 0.3879 | 0.8709 | | 0.3837 | 10.0 | 3750 | 0.3775 | 0.8685 | | 0.3494 | 11.0 | 4125 | 0.3737 | 0.8712 | | 0.3812 | 12.0 | 4500 | 0.3492 | 0.8776 | | 0.3355 | 13.0 | 4875 | 0.3613 | 0.8733 | | 0.3676 | 14.0 | 5250 | 0.3523 | 0.8743 | | 0.2985 | 15.0 | 5625 | 0.3452 | 0.8773 | | 0.35 | 16.0 | 6000 | 0.3203 | 0.8868 | | 0.319 | 17.0 | 6375 | 0.3254 | 0.8829 | | 0.2999 | 18.0 | 6750 | 0.3344 | 0.8822 | | 0.2792 | 19.0 | 7125 | 0.3247 | 0.8823 | | 0.278 | 20.0 | 7500 | 0.3267 | 0.8829 | | 0.2673 | 21.0 | 7875 | 0.3143 | 0.8858 | | 0.2349 | 22.0 | 8250 | 0.3077 | 0.8915 | | 0.2339 | 23.0 | 8625 | 0.3237 | 0.8893 | | 0.244 | 24.0 | 9000 | 0.3048 | 0.8892 | | 0.2633 | 25.0 | 9375 | 0.3125 | 0.8911 | | 0.2425 | 26.0 | 9750 | 0.3141 | 0.8899 | | 0.2234 | 27.0 | 10125 | 0.3178 | 0.8921 | | 0.2127 | 28.0 | 10500 | 0.3187 | 0.8869 | | 0.2186 | 29.0 | 10875 | 0.3314 | 0.8848 | | 0.2152 | 30.0 | 11250 | 0.3261 | 0.8855 | | 0.2045 | 31.0 | 11625 | 0.3236 | 0.8882 | | 0.1676 | 32.0 | 12000 | 0.3313 | 0.8894 | | 0.2126 | 33.0 | 12375 | 0.3271 | 0.8897 | | 0.2166 | 34.0 | 12750 | 0.3262 | 0.8903 | | 0.2067 | 35.0 | 13125 | 0.3091 | 0.8968 | | 0.1695 | 36.0 | 13500 | 0.3198 | 0.8944 | | 0.2089 | 37.0 | 13875 | 0.3150 | 0.8968 | | 0.1639 | 38.0 | 14250 | 0.3300 | 0.8949 | | 0.1667 | 39.0 | 14625 | 0.3269 | 0.8912 | | 0.1874 | 40.0 | 15000 | 0.3275 | 0.8943 | | 0.1798 | 41.0 | 15375 | 0.3396 | 0.8948 | | 0.1608 | 42.0 | 15750 | 0.3187 | 0.8957 | | 0.1599 | 43.0 | 16125 | 0.3281 | 0.8973 | | 0.1766 | 44.0 | 16500 | 0.3434 | 0.8933 | | 0.1812 | 45.0 | 16875 | 0.3471 | 0.8954 | | 0.1773 | 46.0 | 17250 | 0.3268 | 0.8965 | | 0.1732 | 47.0 | 17625 | 0.3316 | 0.8942 | | 0.1577 | 48.0 | 18000 | 0.3384 | 0.8975 | | 0.1594 | 49.0 | 18375 | 0.3453 | 0.8934 | | 0.1508 | 50.0 | 18750 | 0.3459 | 0.8931 | | 0.1473 | 51.0 | 19125 | 0.3516 | 0.8933 | | 0.1516 | 52.0 | 19500 | 0.3428 | 0.8948 | | 0.1362 | 53.0 | 19875 | 0.3400 | 0.8945 | | 0.1615 | 54.0 | 20250 | 0.3458 | 0.8939 | | 0.1722 | 55.0 | 20625 | 0.3311 | 0.8958 | | 0.1351 | 56.0 | 21000 | 0.3484 | 0.8962 | | 0.1499 | 57.0 | 21375 | 0.3311 | 0.899 | | 0.1607 | 58.0 | 21750 | 0.3354 | 0.8987 | | 0.1703 | 59.0 | 22125 | 0.3525 | 0.8912 | | 0.1471 | 60.0 | 22500 | 0.3531 | 0.8948 | | 0.1483 | 61.0 | 22875 | 0.3546 | 0.8948 | | 0.174 | 62.0 | 23250 | 0.3503 | 0.8968 | | 0.1443 | 63.0 | 23625 | 0.3633 | 0.8957 | | 0.1387 | 64.0 | 24000 | 0.3437 | 0.8992 | | 0.1433 | 65.0 | 24375 | 0.3586 | 0.8982 | | 0.1508 | 66.0 | 24750 | 0.3450 | 0.8998 | | 0.1556 | 67.0 | 25125 | 0.3691 | 0.8977 | | 0.1226 | 68.0 | 25500 | 0.3393 | 0.9015 | | 0.1188 | 69.0 | 25875 | 0.3463 | 0.8997 | | 0.1344 | 70.0 | 26250 | 0.3438 | 0.9011 | | 0.1242 | 71.0 | 26625 | 0.3518 | 0.8968 | | 0.1292 | 72.0 | 27000 | 0.3533 | 0.8977 | | 0.1654 | 73.0 | 27375 | 0.3524 | 0.8965 | | 0.141 | 74.0 | 27750 | 0.3461 | 0.8982 | | 0.149 | 75.0 | 28125 | 0.3564 | 0.9025 | | 0.1129 | 76.0 | 28500 | 0.3728 | 0.8981 | | 0.1213 | 77.0 | 28875 | 0.3796 | 0.8975 | | 0.1394 | 78.0 | 29250 | 0.3666 | 0.8982 | | 0.1187 | 79.0 | 29625 | 0.3688 | 0.8972 | | 0.137 | 80.0 | 30000 | 0.3535 | 0.9008 | | 0.1253 | 81.0 | 30375 | 0.3509 | 0.9022 | | 0.1275 | 82.0 | 30750 | 0.3735 | 0.8976 | | 0.0954 | 83.0 | 31125 | 0.3688 | 0.8985 | | 0.1201 | 84.0 | 31500 | 0.3540 | 0.9022 | | 0.1051 | 85.0 | 31875 | 0.3733 | 0.8999 | | 0.1201 | 86.0 | 32250 | 0.3647 | 0.9035 | | 0.1251 | 87.0 | 32625 | 0.3699 | 0.8998 | | 0.1141 | 88.0 | 33000 | 0.3734 | 0.8996 | | 0.1116 | 89.0 | 33375 | 0.3836 | 0.8981 | | 0.1173 | 90.0 | 33750 | 0.3769 | 0.9003 | | 0.1071 | 91.0 | 34125 | 0.3654 | 0.8998 | | 0.1018 | 92.0 | 34500 | 0.3749 | 0.9018 | | 0.093 | 93.0 | 34875 | 0.3684 | 0.9012 | | 0.1298 | 94.0 | 35250 | 0.3627 | 0.9032 | | 0.0994 | 95.0 | 35625 | 0.3845 | 0.8996 | | 0.124 | 96.0 | 36000 | 0.3669 | 0.9028 | | 0.1044 | 97.0 | 36375 | 0.3742 | 0.8997 | | 0.1194 | 98.0 | 36750 | 0.3723 | 0.9057 | | 0.0962 | 99.0 | 37125 | 0.3777 | 0.9005 | | 0.1028 | 100.0 | 37500 | 0.3780 | 0.9048 | | 0.1102 | 101.0 | 37875 | 0.3919 | 0.9031 | | 0.0849 | 102.0 | 38250 | 0.3768 | 0.9017 | | 0.1138 | 103.0 | 38625 | 0.3685 | 0.9022 | | 0.1422 | 104.0 | 39000 | 0.3829 | 0.9041 | | 0.1094 | 105.0 | 39375 | 0.3925 | 0.8958 | | 0.1306 | 106.0 | 39750 | 0.3747 | 0.9048 | | 0.1059 | 107.0 | 40125 | 0.3814 | 0.905 | | 0.1147 | 108.0 | 40500 | 0.3725 | 0.9028 | | 0.1086 | 109.0 | 40875 | 0.3697 | 0.9032 | | 0.1001 | 110.0 | 41250 | 0.3895 | 0.9007 | | 0.0994 | 111.0 | 41625 | 0.3813 | 0.9048 | | 0.1159 | 112.0 | 42000 | 0.3858 | 0.8995 | | 0.0952 | 113.0 | 42375 | 0.4012 | 0.9018 | | 0.1064 | 114.0 | 42750 | 0.3858 | 0.9035 | | 0.0932 | 115.0 | 43125 | 0.3854 | 0.9072 | | 0.1131 | 116.0 | 43500 | 0.3942 | 0.9037 | | 0.1058 | 117.0 | 43875 | 0.4075 | 0.901 | | 0.1056 | 118.0 | 44250 | 0.3975 | 0.9016 | | 0.087 | 119.0 | 44625 | 0.3816 | 0.902 | | 0.1009 | 120.0 | 45000 | 0.3845 | 0.9035 | | 0.0872 | 121.0 | 45375 | 0.3900 | 0.9033 | | 0.0894 | 122.0 | 45750 | 0.4058 | 0.9019 | | 0.0878 | 123.0 | 46125 | 0.3972 | 0.9052 | | 0.1163 | 124.0 | 46500 | 0.3846 | 0.9066 | | 0.1003 | 125.0 | 46875 | 0.3934 | 0.9015 | | 0.0935 | 126.0 | 47250 | 0.3954 | 0.903 | | 0.0883 | 127.0 | 47625 | 0.3901 | 0.9067 | | 0.1109 | 128.0 | 48000 | 0.3800 | 0.9038 | | 0.0881 | 129.0 | 48375 | 0.4080 | 0.9021 | | 0.0912 | 130.0 | 48750 | 0.3925 | 0.9054 | | 0.0829 | 131.0 | 49125 | 0.4187 | 0.8992 | | 0.079 | 132.0 | 49500 | 0.3905 | 0.9041 | | 0.0788 | 133.0 | 49875 | 0.4077 | 0.9045 | | 0.085 | 134.0 | 50250 | 0.3973 | 0.9002 | | 0.0804 | 135.0 | 50625 | 0.3904 | 0.9063 | | 0.1052 | 136.0 | 51000 | 0.4110 | 0.9052 | | 0.082 | 137.0 | 51375 | 0.4106 | 0.9046 | | 0.0947 | 138.0 | 51750 | 0.3886 | 0.9089 | | 0.0908 | 139.0 | 52125 | 0.3948 | 0.9066 | | 0.0765 | 140.0 | 52500 | 0.4146 | 0.9028 | | 0.0688 | 141.0 | 52875 | 0.4000 | 0.9026 | | 0.1023 | 142.0 | 53250 | 0.4014 | 0.9071 | | 0.0751 | 143.0 | 53625 | 0.3932 | 0.9058 | | 0.0903 | 144.0 | 54000 | 0.4060 | 0.9052 | | 0.0875 | 145.0 | 54375 | 0.3941 | 0.9056 | | 0.0896 | 146.0 | 54750 | 0.3962 | 0.9067 | | 0.0666 | 147.0 | 55125 | 0.4053 | 0.9045 | | 0.0871 | 148.0 | 55500 | 0.4152 | 0.9019 | | 0.0924 | 149.0 | 55875 | 0.4091 | 0.906 | | 0.0865 | 150.0 | 56250 | 0.4197 | 0.9006 | | 0.1072 | 151.0 | 56625 | 0.4019 | 0.9052 | | 0.0956 | 152.0 | 57000 | 0.3962 | 0.9061 | | 0.0571 | 153.0 | 57375 | 0.3999 | 0.908 | | 0.0941 | 154.0 | 57750 | 0.4014 | 0.9067 | | 0.0854 | 155.0 | 58125 | 0.3906 | 0.9059 | | 0.0943 | 156.0 | 58500 | 0.4020 | 0.9064 | | 0.0933 | 157.0 | 58875 | 0.4166 | 0.9072 | | 0.0863 | 158.0 | 59250 | 0.3903 | 0.9089 | | 0.0818 | 159.0 | 59625 | 0.4006 | 0.9043 | | 0.0834 | 160.0 | 60000 | 0.4020 | 0.9054 | | 0.0919 | 161.0 | 60375 | 0.3927 | 0.9079 | | 0.089 | 162.0 | 60750 | 0.3889 | 0.9083 | | 0.0783 | 163.0 | 61125 | 0.4030 | 0.907 | | 0.0724 | 164.0 | 61500 | 0.4124 | 0.9066 | | 0.0643 | 165.0 | 61875 | 0.4051 | 0.9057 | | 0.0638 | 166.0 | 62250 | 0.4058 | 0.907 | | 0.0701 | 167.0 | 62625 | 0.3910 | 0.9077 | | 0.0515 | 168.0 | 63000 | 0.3881 | 0.9083 | | 0.0859 | 169.0 | 63375 | 0.3905 | 0.9098 | | 0.0799 | 170.0 | 63750 | 0.3997 | 0.9067 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.2 - Datasets 2.19.0 - Tokenizers 0.19.1