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cifar100-resnet-50

Image Classification
Safetensors
resnet
vision
Generated from Trainer
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metadata
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - image-classification
  - vision
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet-50
    results: []

resnet-50

This model is a fine-tuned version of microsoft/resnet-50 on the cifar100 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5732
  • Accuracy: 0.8286

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: 300

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.5562 1.0 333 4.5546 0.1308
4.4468 2.0 666 4.4367 0.145
4.3538 3.0 999 4.3131 0.1859
4.2283 4.0 1332 4.1398 0.2269
4.0124 5.0 1665 3.9074 0.2647
3.8102 6.0 1998 3.6060 0.3
3.5657 7.0 2331 3.3058 0.3334
3.3654 8.0 2664 3.0348 0.3695
3.1954 9.0 2997 2.7789 0.3996
3.0844 10.0 3330 2.5592 0.4416
2.9031 11.0 3663 2.3489 0.4707
2.7383 12.0 3996 2.1829 0.4907
2.6365 13.0 4329 2.0404 0.5173
2.4545 14.0 4662 1.9062 0.5402
2.3891 15.0 4995 1.7761 0.5673
2.2835 16.0 5328 1.6824 0.5783
2.3137 17.0 5661 1.6064 0.5932
2.1022 18.0 5994 1.5257 0.6081
2.0252 19.0 6327 1.4517 0.6221
2.2526 20.0 6660 1.3901 0.6279
1.963 21.0 6993 1.3430 0.6425
1.9656 22.0 7326 1.3013 0.6413
1.8864 23.0 7659 1.2617 0.6524
1.887 24.0 7992 1.2376 0.6584
1.7976 25.0 8325 1.1766 0.6717
1.7482 26.0 8658 1.1570 0.6758
1.7816 27.0 8991 1.1237 0.6834
1.7477 28.0 9324 1.1027 0.6878
1.7196 29.0 9657 1.0760 0.6899
1.7635 30.0 9990 1.0600 0.6934
1.6424 31.0 10323 1.0388 0.6975
1.6704 32.0 10656 1.0172 0.7053
1.6393 33.0 10989 1.0008 0.7106
1.5795 34.0 11322 0.9909 0.7126
1.6104 35.0 11655 0.9561 0.7199
1.587 36.0 11988 0.9593 0.7168
1.6046 37.0 12321 0.9299 0.7267
1.5859 38.0 12654 0.9168 0.7271
1.5149 39.0 12987 0.9122 0.7301
1.6676 40.0 13320 0.8964 0.7358
1.4889 41.0 13653 0.8964 0.7345
1.4958 42.0 13986 0.8821 0.7374
1.4397 43.0 14319 0.8733 0.7441
1.4745 44.0 14652 0.8683 0.7397
1.4804 45.0 14985 0.8614 0.7429
1.4372 46.0 15318 0.8450 0.7472
1.4181 47.0 15651 0.8381 0.7479
1.4067 48.0 15984 0.8238 0.7533
1.4155 49.0 16317 0.8283 0.7471
1.5512 50.0 16650 0.8113 0.7546
1.3912 51.0 16983 0.8014 0.7582
1.4082 52.0 17316 0.8070 0.7574
1.4463 53.0 17649 0.7986 0.7588
1.3902 54.0 17982 0.7865 0.7629
1.3382 55.0 18315 0.7810 0.7634
1.3448 56.0 18648 0.7727 0.7652
1.283 57.0 18981 0.7681 0.7664
1.2979 58.0 19314 0.7637 0.7704
1.3176 59.0 19647 0.7614 0.7712
1.4151 60.0 19980 0.7597 0.7671
1.3055 61.0 20313 0.7513 0.7697
1.3024 62.0 20646 0.7510 0.7728
1.3113 63.0 20979 0.7433 0.7731
1.2962 64.0 21312 0.7500 0.77
1.28 65.0 21645 0.7307 0.7781
1.2518 66.0 21978 0.7301 0.7773
1.2792 67.0 22311 0.7286 0.7762
1.2137 68.0 22644 0.7210 0.7781
1.2598 69.0 22977 0.7219 0.7784
1.4021 70.0 23310 0.7204 0.7803
1.3074 71.0 23643 0.7066 0.7822
1.2205 72.0 23976 0.7121 0.7808
1.2696 73.0 24309 0.7162 0.7799
1.2083 74.0 24642 0.7031 0.786
1.2186 75.0 24975 0.6934 0.7876
1.2252 76.0 25308 0.7062 0.7849
1.303 77.0 25641 0.7015 0.7846
1.2131 78.0 25974 0.6964 0.7861
1.1887 79.0 26307 0.6877 0.7867
1.6358 80.0 26640 0.6987 0.7872
1.1976 81.0 26973 0.6891 0.7887
1.1602 82.0 27306 0.6797 0.7894
1.2226 83.0 27639 0.6890 0.7883
1.2658 84.0 27972 0.6824 0.7893
1.0837 85.0 28305 0.6840 0.7902
1.112 86.0 28638 0.6754 0.7933
1.2667 87.0 28971 0.6650 0.7964
1.1847 88.0 29304 0.6716 0.7926
1.1431 89.0 29637 0.6763 0.7934
1.4006 90.0 29970 0.6723 0.7945
1.1093 91.0 30303 0.6668 0.7945
1.1468 92.0 30636 0.6612 0.7958
1.1783 93.0 30969 0.6685 0.795
1.1586 94.0 31302 0.6570 0.7964
1.1325 95.0 31635 0.6605 0.7946
1.1619 96.0 31968 0.6538 0.7963
1.1547 97.0 32301 0.6510 0.7992
1.198 98.0 32634 0.6495 0.8014
1.0816 99.0 32967 0.6501 0.8008
1.1854 100.0 33300 0.6525 0.8007
1.1589 101.0 33633 0.6484 0.8004
1.1621 102.0 33966 0.6456 0.8028
1.1066 103.0 34299 0.6549 0.8006
1.1108 104.0 34632 0.6475 0.8016
1.1329 105.0 34965 0.6420 0.802
1.084 106.0 35298 0.6432 0.8011
1.0535 107.0 35631 0.6415 0.8026
1.0708 108.0 35964 0.6415 0.8006
1.0657 109.0 36297 0.6398 0.8033
1.1575 110.0 36630 0.6462 0.802
1.0678 111.0 36963 0.6390 0.8028
1.1565 112.0 37296 0.6506 0.8012
1.0379 113.0 37629 0.6424 0.8023
1.0942 114.0 37962 0.6378 0.8032
1.0977 115.0 38295 0.6248 0.8069
1.1348 116.0 38628 0.6264 0.8082
1.0204 117.0 38961 0.6255 0.808
1.0201 118.0 39294 0.6240 0.8088
1.1539 119.0 39627 0.6252 0.8064
1.3025 120.0 39960 0.6305 0.805
1.0533 121.0 40293 0.6284 0.8065
0.9733 122.0 40626 0.6237 0.8075
1.0752 123.0 40959 0.6218 0.8098
1.1421 124.0 41292 0.6187 0.807
0.9842 125.0 41625 0.6294 0.8078
1.06 126.0 41958 0.6174 0.8094
1.1292 127.0 42291 0.6206 0.8084
1.0878 128.0 42624 0.6144 0.8103
1.0766 129.0 42957 0.6126 0.8104
1.2749 130.0 43290 0.6123 0.8106
1.1147 131.0 43623 0.6128 0.8105
1.0357 132.0 43956 0.6060 0.8138
1.0424 133.0 44289 0.6062 0.8146
1.0532 134.0 44622 0.6124 0.8121
1.0163 135.0 44955 0.6136 0.8123
1.0789 136.0 45288 0.6144 0.8122
0.9845 137.0 45621 0.6118 0.8114
1.0238 138.0 45954 0.6074 0.8123
1.0287 139.0 46287 0.6099 0.8135
1.1634 140.0 46620 0.6043 0.8151
1.0906 141.0 46953 0.6071 0.8134
1.0672 142.0 47286 0.6001 0.8168
1.0423 143.0 47619 0.6077 0.8144
1.1038 144.0 47952 0.6028 0.8155
0.9353 145.0 48285 0.6065 0.8117
1.0238 146.0 48618 0.5979 0.8151
1.0313 147.0 48951 0.6022 0.8149
1.0897 148.0 49284 0.6008 0.8179
0.9711 149.0 49617 0.6040 0.8148
1.2002 150.0 49950 0.6018 0.8162
1.0154 151.0 50283 0.6042 0.816
1.0561 152.0 50616 0.6042 0.8145
0.9962 153.0 50949 0.6011 0.8158
1.0812 154.0 51282 0.5961 0.8165
1.0307 155.0 51615 0.6054 0.8152
0.991 156.0 51948 0.6019 0.814
1.0396 157.0 52281 0.6014 0.815
1.0524 158.0 52614 0.6015 0.8164
0.9873 159.0 52947 0.6001 0.8152
1.0471 160.0 53280 0.5988 0.8165
0.9178 161.0 53613 0.5936 0.8185
0.9738 162.0 53946 0.5894 0.8205
1.0487 163.0 54279 0.5969 0.8161
1.0434 164.0 54612 0.5946 0.8173
0.9916 165.0 54945 0.5960 0.8194
0.9596 166.0 55278 0.5890 0.8194
1.0006 167.0 55611 0.5910 0.8176
0.99 168.0 55944 0.5901 0.8195
1.0125 169.0 56277 0.5949 0.8185
1.0714 170.0 56610 0.5907 0.8193
1.011 171.0 56943 0.5952 0.8201
0.9099 172.0 57276 0.5905 0.8169
0.9879 173.0 57609 0.5955 0.8201
1.0559 174.0 57942 0.5892 0.8197
1.0002 175.0 58275 0.5914 0.8201
0.9461 176.0 58608 0.5866 0.8214
0.9624 177.0 58941 0.5870 0.8228
0.9952 178.0 59274 0.5920 0.8199
1.0415 179.0 59607 0.5926 0.8193
1.0416 180.0 59940 0.5901 0.8206
0.9467 181.0 60273 0.5911 0.8216
0.9783 182.0 60606 0.5832 0.8206
0.9147 183.0 60939 0.5881 0.8223
0.9848 184.0 61272 0.5898 0.8218
0.9454 185.0 61605 0.5916 0.8201
1.0287 186.0 61938 0.5880 0.8222
0.9336 187.0 62271 0.5856 0.8221
1.0148 188.0 62604 0.5903 0.8205
0.9184 189.0 62937 0.5811 0.8217
1.2194 190.0 63270 0.5852 0.8214
0.9717 191.0 63603 0.5873 0.8204
1.0003 192.0 63936 0.5836 0.8239
0.9657 193.0 64269 0.5806 0.8243
0.9865 194.0 64602 0.5839 0.8225
0.9642 195.0 64935 0.5850 0.8219
0.9839 196.0 65268 0.5815 0.8246
0.999 197.0 65601 0.5787 0.8252
0.9957 198.0 65934 0.5854 0.821
0.9442 199.0 66267 0.5894 0.8189
1.1311 200.0 66600 0.5785 0.8235
0.9542 201.0 66933 0.5783 0.824
0.9352 202.0 67266 0.5811 0.8231
0.9764 203.0 67599 0.5898 0.8198
0.9557 204.0 67932 0.5757 0.8239
0.9073 205.0 68265 0.5838 0.8227
0.9087 206.0 68598 0.5784 0.8234
0.9194 207.0 68931 0.5789 0.8212
0.9406 208.0 69264 0.5724 0.826
0.8866 209.0 69597 0.5773 0.8247
1.0926 210.0 69930 0.5830 0.8232
0.9185 211.0 70263 0.5780 0.8246
0.9636 212.0 70596 0.5779 0.8252
0.9503 213.0 70929 0.5781 0.8242
0.9006 214.0 71262 0.5856 0.8237
0.9294 215.0 71595 0.5737 0.8244
1.0017 216.0 71928 0.5802 0.8245
0.9228 217.0 72261 0.5796 0.8243
0.9644 218.0 72594 0.5859 0.8219
0.8991 219.0 72927 0.5795 0.8235
1.1149 220.0 73260 0.5778 0.8253
0.9295 221.0 73593 0.5785 0.8251
0.9376 222.0 73926 0.5770 0.8255
0.8995 223.0 74259 0.5791 0.8251
0.8994 224.0 74592 0.5716 0.8266
0.908 225.0 74925 0.5742 0.825
0.9579 226.0 75258 0.5762 0.8234
0.9263 227.0 75591 0.5745 0.8247
0.9343 228.0 75924 0.5736 0.8273
0.8955 229.0 76257 0.5760 0.8248
1.063 230.0 76590 0.5766 0.8259
0.9331 231.0 76923 0.5766 0.826
0.9409 232.0 77256 0.5826 0.8242
0.9361 233.0 77589 0.5717 0.8279
0.922 234.0 77922 0.5722 0.8262
0.9189 235.0 78255 0.5670 0.8278
0.835 236.0 78588 0.5674 0.8274
1.0082 237.0 78921 0.5738 0.8256
0.9356 238.0 79254 0.5701 0.8277
0.888 239.0 79587 0.5701 0.8267
1.2103 240.0 79920 0.5770 0.8237
0.9076 241.0 80253 0.5892 0.8223
0.8956 242.0 80586 0.5717 0.8264
0.8968 243.0 80919 0.5747 0.8238
0.9055 244.0 81252 0.5746 0.8246
0.8601 245.0 81585 0.5729 0.8269
0.9811 246.0 81918 0.5691 0.8257
0.9001 247.0 82251 0.5832 0.8247
0.9668 248.0 82584 0.5769 0.8268
0.9281 249.0 82917 0.5740 0.8257
0.9167 250.0 83250 0.5753 0.8265
1.0039 251.0 83583 0.5730 0.8273
0.9624 252.0 83916 0.5667 0.8291
0.8988 253.0 84249 0.5751 0.8255
1.0041 254.0 84582 0.5718 0.8267
0.8924 255.0 84915 0.5741 0.8253
0.9587 256.0 85248 0.5665 0.8277
0.959 257.0 85581 0.5679 0.8292
0.8092 258.0 85914 0.5719 0.8281
0.9023 259.0 86247 0.5692 0.8282
1.0531 260.0 86580 0.5707 0.8271
0.9112 261.0 86913 0.5704 0.8259
0.8781 262.0 87246 0.5741 0.826
0.8708 263.0 87579 0.5654 0.829
0.8706 264.0 87912 0.5743 0.8259
0.8743 265.0 88245 0.5671 0.8283
0.9297 266.0 88578 0.5726 0.8294
0.9017 267.0 88911 0.5752 0.8282
0.9106 268.0 89244 0.5732 0.8268
0.8829 269.0 89577 0.5750 0.827
1.1097 270.0 89910 0.5733 0.8277
0.9004 271.0 90243 0.5688 0.8291
0.8734 272.0 90576 0.5707 0.8275
0.8877 273.0 90909 0.5711 0.8265
0.9416 274.0 91242 0.5672 0.8273
0.9037 275.0 91575 0.5689 0.8284
0.8345 276.0 91908 0.5695 0.8299
0.8627 277.0 92241 0.5740 0.8277
0.8964 278.0 92574 0.5756 0.8262
0.8793 279.0 92907 0.5728 0.8268
1.0857 280.0 93240 0.5671 0.8306
0.8668 281.0 93573 0.5685 0.8305
0.9051 282.0 93906 0.5751 0.8299
0.9183 283.0 94239 0.5746 0.8267
0.9507 284.0 94572 0.5699 0.8262
0.8783 285.0 94905 0.5745 0.8289
0.8942 286.0 95238 0.5745 0.8262
0.8369 287.0 95571 0.5688 0.8288
0.9278 288.0 95904 0.5660 0.8287
0.9285 289.0 96237 0.5707 0.8285
0.9858 290.0 96570 0.5668 0.829
0.8783 291.0 96903 0.5781 0.8261
0.9269 292.0 97236 0.5681 0.8277
0.9153 293.0 97569 0.5691 0.8271
0.8814 294.0 97902 0.5692 0.8273
0.915 295.0 98235 0.5755 0.8269
0.9032 296.0 98568 0.5806 0.8228
0.9006 297.0 98901 0.5719 0.8273
0.8629 298.0 99234 0.5732 0.8262
0.887 299.0 99567 0.5658 0.828
1.0707 300.0 99900 0.5732 0.8286

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.2