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FASHION-vision

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metadata
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 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4566
  • Accuracy: 0.9073

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: 300

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.0665 1.0 375 2.0520 0.5303
1.3346 2.0 750 1.3436 0.7141
1.0001 3.0 1125 0.9733 0.7697
0.7604 4.0 1500 0.7950 0.7964
0.651 5.0 1875 0.6824 0.8224
0.5874 6.0 2250 0.6112 0.8266
0.5543 7.0 2625 0.5375 0.8391
0.504 8.0 3000 0.4992 0.8472
0.5299 9.0 3375 0.4514 0.8597
0.4556 10.0 3750 0.4251 0.8616
0.3919 11.0 4125 0.4200 0.8592
0.3716 12.0 4500 0.3959 0.8643
0.348 13.0 4875 0.3788 0.8698
0.3476 14.0 5250 0.3631 0.8717
0.3438 15.0 5625 0.3598 0.8752
0.3438 16.0 6000 0.3482 0.8769
0.2837 17.0 6375 0.3464 0.8814
0.3265 18.0 6750 0.3626 0.8697
0.276 19.0 7125 0.3304 0.8846
0.307 20.0 7500 0.3348 0.8838
0.317 21.0 7875 0.3291 0.885
0.3304 22.0 8250 0.3252 0.8844
0.2476 23.0 8625 0.3164 0.8888
0.3002 24.0 9000 0.3332 0.8801
0.2899 25.0 9375 0.3259 0.8835
0.264 26.0 9750 0.3254 0.8848
0.2713 27.0 10125 0.3120 0.8901
0.2392 28.0 10500 0.3280 0.8842
0.2299 29.0 10875 0.3202 0.8861
0.2494 30.0 11250 0.3310 0.8888
0.24 31.0 11625 0.3334 0.8821
0.1909 32.0 12000 0.3239 0.8892
0.2784 33.0 12375 0.3418 0.8818
0.2146 34.0 12750 0.3290 0.885
0.2113 35.0 13125 0.3377 0.8844
0.187 36.0 13500 0.3118 0.8915
0.2356 37.0 13875 0.3338 0.8837
0.212 38.0 14250 0.3225 0.8955
0.1891 39.0 14625 0.3241 0.8914
0.1825 40.0 15000 0.3227 0.8912
0.1999 41.0 15375 0.3258 0.8954
0.2248 42.0 15750 0.3196 0.8967
0.2047 43.0 16125 0.3301 0.8916
0.1734 44.0 16500 0.3314 0.8922
0.2015 45.0 16875 0.3161 0.8927
0.192 46.0 17250 0.3346 0.8915
0.1698 47.0 17625 0.3351 0.8928
0.1871 48.0 18000 0.3311 0.892
0.1849 49.0 18375 0.3172 0.8973
0.1804 50.0 18750 0.3360 0.8906
0.2109 51.0 19125 0.3325 0.8924
0.1739 52.0 19500 0.3476 0.8921
0.1853 53.0 19875 0.3473 0.8856
0.1733 54.0 20250 0.3461 0.8939
0.166 55.0 20625 0.3455 0.894
0.1459 56.0 21000 0.3602 0.8918
0.1609 57.0 21375 0.3481 0.8911
0.1591 58.0 21750 0.3587 0.8924
0.1613 59.0 22125 0.3413 0.8955
0.1294 60.0 22500 0.3239 0.9012
0.1521 61.0 22875 0.3337 0.898
0.1574 62.0 23250 0.3511 0.8902
0.187 63.0 23625 0.3423 0.8942
0.1674 64.0 24000 0.3452 0.8967
0.1359 65.0 24375 0.3572 0.8942
0.1443 66.0 24750 0.3602 0.894
0.1427 67.0 25125 0.3513 0.8995
0.1431 68.0 25500 0.3676 0.8945
0.1421 69.0 25875 0.3524 0.897
0.1422 70.0 26250 0.3651 0.894
0.1462 71.0 26625 0.3463 0.8996
0.1396 72.0 27000 0.3546 0.8969
0.1705 73.0 27375 0.3696 0.8937
0.1377 74.0 27750 0.3731 0.8902
0.1637 75.0 28125 0.3612 0.8948
0.1555 76.0 28500 0.3654 0.8977
0.1319 77.0 28875 0.3650 0.8967
0.138 78.0 29250 0.3723 0.8957
0.1292 79.0 29625 0.3750 0.8935
0.1356 80.0 30000 0.3789 0.894
0.1118 81.0 30375 0.3815 0.8955
0.1205 82.0 30750 0.3631 0.8979
0.1212 83.0 31125 0.3582 0.8972
0.1414 84.0 31500 0.3715 0.9005
0.139 85.0 31875 0.3734 0.8991
0.1055 86.0 32250 0.3782 0.8991
0.1072 87.0 32625 0.3676 0.897
0.1089 88.0 33000 0.3871 0.893
0.1376 89.0 33375 0.3615 0.9008
0.178 90.0 33750 0.4045 0.8899
0.1157 91.0 34125 0.3869 0.8952
0.1283 92.0 34500 0.3816 0.8968
0.1282 93.0 34875 0.3847 0.8973
0.1208 94.0 35250 0.3883 0.8947
0.1178 95.0 35625 0.3892 0.8948
0.1317 96.0 36000 0.4091 0.8952
0.1156 97.0 36375 0.3708 0.8986
0.1118 98.0 36750 0.3632 0.9021
0.1138 99.0 37125 0.3704 0.8975
0.0923 100.0 37500 0.3937 0.8998
0.1432 101.0 37875 0.3692 0.8989
0.1014 102.0 38250 0.3817 0.8982
0.1206 103.0 38625 0.3950 0.8951
0.1316 104.0 39000 0.3859 0.8984
0.1544 105.0 39375 0.3900 0.8962
0.1435 106.0 39750 0.3737 0.8976
0.1186 107.0 40125 0.3737 0.8997
0.1179 108.0 40500 0.3859 0.8979
0.1149 109.0 40875 0.3834 0.899
0.1038 110.0 41250 0.3658 0.9036
0.1058 111.0 41625 0.3891 0.9005
0.1266 112.0 42000 0.3828 0.8969
0.1146 113.0 42375 0.4001 0.8918
0.116 114.0 42750 0.4029 0.8939
0.1114 115.0 43125 0.3988 0.8991
0.123 116.0 43500 0.3934 0.8978
0.1151 117.0 43875 0.3915 0.8958
0.1459 118.0 44250 0.3837 0.8983
0.0967 119.0 44625 0.3942 0.8978
0.1157 120.0 45000 0.4096 0.8998
0.11 121.0 45375 0.3997 0.9007
0.104 122.0 45750 0.3807 0.8998
0.0997 123.0 46125 0.4028 0.8966
0.0822 124.0 46500 0.4013 0.8955
0.0918 125.0 46875 0.3977 0.8989
0.0828 126.0 47250 0.4008 0.8986
0.0814 127.0 47625 0.3996 0.8995
0.098 128.0 48000 0.3950 0.8966
0.1019 129.0 48375 0.4040 0.8981
0.1169 130.0 48750 0.3959 0.9001
0.1085 131.0 49125 0.4050 0.8999
0.1144 132.0 49500 0.3851 0.8999
0.078 133.0 49875 0.4251 0.8948
0.1126 134.0 50250 0.4209 0.8968
0.1164 135.0 50625 0.4059 0.8977
0.0827 136.0 51000 0.4044 0.9005
0.1045 137.0 51375 0.3909 0.8996
0.0791 138.0 51750 0.4036 0.8964
0.0783 139.0 52125 0.4175 0.8998
0.0922 140.0 52500 0.4035 0.9019
0.1062 141.0 52875 0.4038 0.8976
0.1096 142.0 53250 0.4117 0.8994
0.1017 143.0 53625 0.4025 0.9024
0.077 144.0 54000 0.4227 0.9015
0.1038 145.0 54375 0.3858 0.9004
0.1042 146.0 54750 0.3862 0.9005
0.0697 147.0 55125 0.4004 0.9026
0.1098 148.0 55500 0.4259 0.8995
0.0792 149.0 55875 0.4105 0.9002
0.0785 150.0 56250 0.4291 0.8995
0.0948 151.0 56625 0.4191 0.9022
0.0758 152.0 57000 0.4425 0.8948
0.084 153.0 57375 0.3989 0.9012
0.0886 154.0 57750 0.4022 0.8993
0.0839 155.0 58125 0.4182 0.8993
0.0944 156.0 58500 0.4232 0.8985
0.0864 157.0 58875 0.4235 0.8986
0.0945 158.0 59250 0.4273 0.9005
0.0791 159.0 59625 0.4271 0.8989
0.0916 160.0 60000 0.4296 0.8994
0.0918 161.0 60375 0.4203 0.9002
0.0947 162.0 60750 0.4044 0.9023
0.1048 163.0 61125 0.4092 0.9023
0.1076 164.0 61500 0.4169 0.902
0.0932 165.0 61875 0.4470 0.8992
0.0614 166.0 62250 0.4403 0.8971
0.1037 167.0 62625 0.4328 0.8996
0.0983 168.0 63000 0.4227 0.9016
0.0902 169.0 63375 0.4249 0.901
0.0791 170.0 63750 0.4454 0.8996
0.0909 171.0 64125 0.4213 0.9019
0.0914 172.0 64500 0.4173 0.9042
0.1065 173.0 64875 0.4292 0.8992
0.0996 174.0 65250 0.4199 0.9034
0.0949 175.0 65625 0.4369 0.8972
0.0668 176.0 66000 0.4333 0.8993
0.09 177.0 66375 0.4366 0.8998
0.0931 178.0 66750 0.4459 0.9008
0.0655 179.0 67125 0.4323 0.8998
0.0709 180.0 67500 0.4277 0.9002
0.0944 181.0 67875 0.4428 0.9011
0.0506 182.0 68250 0.4304 0.9012
0.0733 183.0 68625 0.4333 0.8993
0.0864 184.0 69000 0.4618 0.8965
0.0936 185.0 69375 0.4417 0.9005
0.0872 186.0 69750 0.4424 0.899
0.1057 187.0 70125 0.4253 0.9036
0.0782 188.0 70500 0.4382 0.9004
0.0837 189.0 70875 0.4474 0.8987
0.0887 190.0 71250 0.4284 0.9048
0.0644 191.0 71625 0.4488 0.9016
0.0688 192.0 72000 0.4485 0.8993
0.0814 193.0 72375 0.4329 0.9023
0.076 194.0 72750 0.4330 0.9019
0.1074 195.0 73125 0.4217 0.9012
0.0673 196.0 73500 0.4384 0.9004
0.0719 197.0 73875 0.4495 0.902
0.0896 198.0 74250 0.4529 0.8996
0.0907 199.0 74625 0.4422 0.9021
0.0731 200.0 75000 0.4476 0.8992
0.0939 201.0 75375 0.4318 0.9028
0.0737 202.0 75750 0.4276 0.9039
0.0813 203.0 76125 0.4491 0.8984
0.0769 204.0 76500 0.4442 0.9024
0.0802 205.0 76875 0.4481 0.9027
0.0857 206.0 77250 0.4324 0.9009
0.0764 207.0 77625 0.4464 0.9051
0.0862 208.0 78000 0.4607 0.899
0.0754 209.0 78375 0.4600 0.901
0.0625 210.0 78750 0.4348 0.906
0.0779 211.0 79125 0.4489 0.9057
0.0666 212.0 79500 0.4530 0.8992
0.0644 213.0 79875 0.4470 0.9019
0.0819 214.0 80250 0.4517 0.9018
0.0524 215.0 80625 0.4556 0.9008
0.0847 216.0 81000 0.4529 0.9007
0.0671 217.0 81375 0.4703 0.8978
0.0804 218.0 81750 0.4798 0.8995
0.065 219.0 82125 0.4528 0.9014
0.0669 220.0 82500 0.4454 0.9069
0.0705 221.0 82875 0.4272 0.9054
0.0738 222.0 83250 0.4662 0.9015
0.099 223.0 83625 0.4480 0.9011
0.0866 224.0 84000 0.4577 0.9012
0.0609 225.0 84375 0.4579 0.8996
0.1066 226.0 84750 0.4378 0.9038
0.0697 227.0 85125 0.4501 0.9029
0.0722 228.0 85500 0.4610 0.9028
0.0679 229.0 85875 0.4627 0.8998
0.0729 230.0 86250 0.4477 0.9008
0.0847 231.0 86625 0.4602 0.9011
0.0626 232.0 87000 0.4569 0.9008
0.0545 233.0 87375 0.4615 0.9023
0.0725 234.0 87750 0.4563 0.9
0.0824 235.0 88125 0.4574 0.9042
0.0617 236.0 88500 0.4494 0.9056
0.0675 237.0 88875 0.4713 0.9022
0.0806 238.0 89250 0.4713 0.9027
0.0612 239.0 89625 0.4484 0.9068
0.0741 240.0 90000 0.4454 0.9032
0.0788 241.0 90375 0.4582 0.9016
0.052 242.0 90750 0.4514 0.9018
0.0736 243.0 91125 0.4475 0.9072
0.0651 244.0 91500 0.4669 0.903
0.0642 245.0 91875 0.4558 0.902
0.0577 246.0 92250 0.4756 0.9038
0.0634 247.0 92625 0.4567 0.9031
0.0716 248.0 93000 0.4607 0.9078
0.0765 249.0 93375 0.4485 0.9087
0.0575 250.0 93750 0.4720 0.9029
0.0707 251.0 94125 0.4543 0.9049
0.0713 252.0 94500 0.4745 0.9044
0.0774 253.0 94875 0.4628 0.9039
0.0524 254.0 95250 0.4626 0.9064
0.0716 255.0 95625 0.4630 0.9039
0.0607 256.0 96000 0.4606 0.9049
0.0837 257.0 96375 0.4455 0.9086
0.061 258.0 96750 0.4701 0.9032
0.0536 259.0 97125 0.4752 0.9012
0.0748 260.0 97500 0.4835 0.904
0.0556 261.0 97875 0.4661 0.9055
0.0403 262.0 98250 0.4558 0.9057
0.0633 263.0 98625 0.4738 0.9042
0.0765 264.0 99000 0.4572 0.9061
0.0741 265.0 99375 0.4652 0.9072
0.0642 266.0 99750 0.4638 0.9036
0.0605 267.0 100125 0.4717 0.9026
0.051 268.0 100500 0.4778 0.8997
0.0615 269.0 100875 0.4597 0.9058
0.0683 270.0 101250 0.4479 0.9089
0.0489 271.0 101625 0.4705 0.9028
0.0748 272.0 102000 0.4791 0.9058
0.0722 273.0 102375 0.4709 0.9057
0.0689 274.0 102750 0.4780 0.9022
0.0589 275.0 103125 0.4508 0.9073
0.0681 276.0 103500 0.4658 0.9053
0.0565 277.0 103875 0.4541 0.9045
0.0668 278.0 104250 0.4734 0.9033
0.037 279.0 104625 0.4743 0.9074
0.0508 280.0 105000 0.4579 0.9084
0.052 281.0 105375 0.4767 0.905
0.0628 282.0 105750 0.4637 0.9104
0.0588 283.0 106125 0.4672 0.9083
0.0539 284.0 106500 0.4690 0.9062
0.0725 285.0 106875 0.4680 0.9037
0.0694 286.0 107250 0.4990 0.902
0.065 287.0 107625 0.4609 0.9062
0.0583 288.0 108000 0.4600 0.908
0.0577 289.0 108375 0.4666 0.9054
0.0562 290.0 108750 0.4766 0.905
0.056 291.0 109125 0.4772 0.9026
0.065 292.0 109500 0.4682 0.9039
0.0647 293.0 109875 0.4693 0.9075
0.0522 294.0 110250 0.4686 0.9106
0.0771 295.0 110625 0.4582 0.9061
0.0672 296.0 111000 0.4686 0.9065
0.0568 297.0 111375 0.4637 0.9078
0.0337 298.0 111750 0.4644 0.9058
0.057 299.0 112125 0.4758 0.9048
0.0463 300.0 112500 0.4566 0.9073

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.2
  • Datasets 2.19.0
  • Tokenizers 0.19.1