jialicheng / cifar100_vit-base

huggingface.co
Total runs: 40
24-hour runs: 0
7-day runs: -10
30-day runs: -52
Model's Last Updated: December 29 2024
image-classification

Introduction of cifar100_vit-base

Model Details of cifar100_vit-base

vit-base

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar100 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3142
  • Accuracy: 0.9197
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: 1e-05
  • train_batch_size: 128
  • eval_batch_size: 256
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100
Training results
Training Loss Epoch Step Validation Loss Accuracy
4.0904 1.0 333 4.0142 0.6663
3.5092 2.0 666 3.3563 0.7659
3.0987 3.0 999 2.9060 0.8043
2.7858 4.0 1332 2.5428 0.827
2.4372 5.0 1665 2.2353 0.8428
2.2157 6.0 1998 1.9597 0.8568
1.9573 7.0 2331 1.7241 0.8685
1.841 8.0 2664 1.5210 0.8736
1.6085 9.0 2997 1.3363 0.8832
1.4188 10.0 3330 1.1857 0.8861
1.3424 11.0 3663 1.0522 0.8923
1.1924 12.0 3996 0.9380 0.8983
1.1764 13.0 4329 0.8405 0.8999
1.0548 14.0 4662 0.7641 0.9024
0.9714 15.0 4995 0.6897 0.9069
0.9141 16.0 5328 0.6327 0.9047
0.8937 17.0 5661 0.5862 0.9065
0.79 18.0 5994 0.5389 0.9104
0.6761 19.0 6327 0.5092 0.9075
0.7064 20.0 6660 0.4760 0.9162
0.7224 21.0 6993 0.4502 0.9127
0.712 22.0 7326 0.4289 0.913
0.6541 23.0 7659 0.4088 0.913
0.6338 24.0 7992 0.3914 0.9172
0.6097 25.0 8325 0.3776 0.9182
0.6369 26.0 8658 0.3676 0.9155
0.6007 27.0 8991 0.3632 0.9149
0.6059 28.0 9324 0.3552 0.9187
0.5227 29.0 9657 0.3454 0.9178
0.6712 30.0 9990 0.3375 0.9183
0.5053 31.0 10323 0.3355 0.9171
0.5432 32.0 10656 0.3328 0.917
0.4617 33.0 10989 0.3295 0.9191
0.4784 34.0 11322 0.3250 0.918
0.5088 35.0 11655 0.3188 0.9195
0.5121 36.0 11988 0.3188 0.9172
0.4734 37.0 12321 0.3174 0.9193
0.5554 38.0 12654 0.3108 0.9196
0.4573 39.0 12987 0.3111 0.9203
0.4692 40.0 13320 0.3074 0.9203
0.481 41.0 13653 0.3042 0.922
0.4888 42.0 13986 0.3058 0.921
0.4032 43.0 14319 0.3025 0.9211
0.4731 44.0 14652 0.3063 0.9202
0.4574 45.0 14985 0.3052 0.92
0.3993 46.0 15318 0.3098 0.9215
0.4631 47.0 15651 0.3078 0.9201
0.409 48.0 15984 0.3056 0.9197
0.4584 49.0 16317 0.3060 0.9208
0.3853 50.0 16650 0.3061 0.9208
0.3836 51.0 16983 0.3072 0.9216
0.3969 52.0 17316 0.3070 0.9197
0.453 53.0 17649 0.3060 0.9188
0.3802 54.0 17982 0.3046 0.9204
0.4191 55.0 18315 0.3075 0.9208
0.4245 56.0 18648 0.3018 0.9205
0.4356 57.0 18981 0.3033 0.9214
0.348 58.0 19314 0.3081 0.9208
0.4232 59.0 19647 0.3058 0.9198
0.3363 60.0 19980 0.3066 0.9195
0.3537 61.0 20313 0.3067 0.9197
0.3613 62.0 20646 0.3065 0.9192
0.4121 63.0 20979 0.3086 0.9211
0.3939 64.0 21312 0.3095 0.9207
0.3616 65.0 21645 0.3061 0.9215
0.3645 66.0 21978 0.3085 0.9197
0.42 67.0 22311 0.3088 0.9191
0.3862 68.0 22644 0.3083 0.9193
0.3519 69.0 22977 0.3103 0.9187
0.4464 70.0 23310 0.3111 0.9192
0.3852 71.0 23643 0.3116 0.919
0.3406 72.0 23976 0.3082 0.9194
0.3785 73.0 24309 0.3071 0.9191
0.3559 74.0 24642 0.3101 0.9194
0.3298 75.0 24975 0.3099 0.9187
0.3596 76.0 25308 0.3099 0.9208
0.3419 77.0 25641 0.3120 0.9201
0.3918 78.0 25974 0.3077 0.9201
0.3571 79.0 26307 0.3119 0.9195
0.3609 80.0 26640 0.3120 0.9195
0.3324 81.0 26973 0.3120 0.9194
0.3387 82.0 27306 0.3118 0.9199
0.441 83.0 27639 0.3117 0.92
0.359 84.0 27972 0.3132 0.9195
0.3106 85.0 28305 0.3131 0.9204
0.3191 86.0 28638 0.3130 0.9201
0.3987 87.0 28971 0.3141 0.9202
0.3327 88.0 29304 0.3138 0.9194
0.3464 89.0 29637 0.3142 0.9207
0.3634 90.0 29970 0.3145 0.9207
0.3123 91.0 30303 0.3133 0.9197
0.3029 92.0 30636 0.3138 0.92
0.3814 93.0 30969 0.3124 0.9192
0.2953 94.0 31302 0.3126 0.9203
0.3475 95.0 31635 0.3141 0.9206
0.3406 96.0 31968 0.3141 0.9197
0.3448 97.0 32301 0.3141 0.9198
0.3687 98.0 32634 0.3137 0.9205
0.345 99.0 32967 0.3144 0.92
0.3582 100.0 33300 0.3142 0.9197
Framework versions
  • Transformers 4.39.3
  • Pytorch 2.2.2+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.2

Runs of jialicheng cifar100_vit-base on huggingface.co

40
Total runs
0
24-hour runs
0
3-day runs
-10
7-day runs
-52
30-day runs

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