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cifar_fine_tuning

Image Classification
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vit
generated_from_trainer
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cifar_fine_tuning / README.md
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - cifar10
metrics:
  - accuracy
model-index:
  - name: cifar_fine_tuning
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar10
          type: cifar10
          config: plain_text
          split: test
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9784

cifar_fine_tuning

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1187
  • Accuracy: 0.9784

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.49 0.03 100 0.4099 0.9073
0.3651 0.06 200 0.2860 0.9267
0.3671 0.1 300 0.4049 0.8832
0.3793 0.13 400 0.3363 0.9
0.4412 0.16 500 0.3392 0.8998
0.2496 0.19 600 0.3285 0.8987
0.4256 0.22 700 0.3622 0.8919
0.4017 0.26 800 0.3954 0.8782
0.3091 0.29 900 0.2375 0.9269
0.17 0.32 1000 0.2150 0.9345
0.2855 0.35 1100 0.2194 0.9314
0.2582 0.38 1200 0.2473 0.9242
0.3127 0.42 1300 0.2789 0.9169
0.3907 0.45 1400 0.3239 0.9046
0.186 0.48 1500 0.2316 0.9315
0.2105 0.51 1600 0.2121 0.9325
0.2539 0.54 1700 0.2014 0.9376
0.3119 0.58 1800 0.2543 0.9218
0.2136 0.61 1900 0.2670 0.9183
0.2067 0.64 2000 0.2062 0.9401
0.242 0.67 2100 0.2852 0.9121
0.1743 0.7 2200 0.1895 0.9414
0.2458 0.74 2300 0.2358 0.9288
0.131 0.77 2400 0.2408 0.9221
0.4146 0.8 2500 0.2134 0.9344
0.3165 0.83 2600 0.2531 0.9248
0.2364 0.86 2700 0.1996 0.9377
0.2476 0.9 2800 0.1971 0.9393
0.298 0.93 2900 0.2114 0.9333
0.181 0.96 3000 0.1894 0.942
0.1499 0.99 3100 0.1819 0.9431
0.1636 1.02 3200 0.2383 0.9271
0.1163 1.06 3300 0.1888 0.9472
0.1179 1.09 3400 0.1788 0.9491
0.1505 1.12 3500 0.2084 0.9407
0.1135 1.15 3600 0.2637 0.9351
0.0996 1.18 3700 0.2281 0.9399
0.1469 1.22 3800 0.1789 0.9485
0.0902 1.25 3900 0.1599 0.9524
0.0456 1.28 4000 0.1803 0.9493
0.1423 1.31 4100 0.1510 0.9562
0.1269 1.34 4200 0.1549 0.9579
0.0713 1.38 4300 0.1833 0.9495
0.0731 1.41 4400 0.1747 0.9511
0.0488 1.44 4500 0.1530 0.9591
0.0538 1.47 4600 0.1870 0.9522
0.0972 1.5 4700 0.1547 0.9562
0.1294 1.54 4800 0.1846 0.9486
0.1035 1.57 4900 0.1609 0.9562
0.1564 1.6 5000 0.1877 0.9521
0.1143 1.63 5100 0.1606 0.9559
0.1239 1.66 5200 0.1457 0.9587
0.107 1.7 5300 0.1815 0.9526
0.0515 1.73 5400 0.1594 0.9568
0.0508 1.76 5500 0.1584 0.9579
0.0308 1.79 5600 0.1640 0.9543
0.0934 1.82 5700 0.1558 0.9535
0.0372 1.86 5800 0.1792 0.9481
0.063 1.89 5900 0.1453 0.9616
0.1284 1.92 6000 0.1719 0.9534
0.0987 1.95 6100 0.1321 0.9649
0.0736 1.98 6200 0.1754 0.9505
0.097 2.02 6300 0.1608 0.9565
0.0062 2.05 6400 0.1687 0.9576
0.0748 2.08 6500 0.2191 0.9488
0.0491 2.11 6600 0.1442 0.9644
0.0467 2.14 6700 0.1525 0.9636
0.042 2.18 6800 0.1440 0.9643
0.0249 2.21 6900 0.1526 0.9627
0.0887 2.24 7000 0.1858 0.9587
0.0438 2.27 7100 0.1485 0.9644
0.0434 2.3 7200 0.1640 0.9623
0.0216 2.34 7300 0.1685 0.9621
0.0496 2.37 7400 0.1612 0.9615
0.0512 2.4 7500 0.1554 0.9635
0.0173 2.43 7600 0.1424 0.9667
0.1097 2.46 7700 0.1691 0.9603
0.0072 2.5 7800 0.1693 0.9588
0.0417 2.53 7900 0.1669 0.9599
0.0624 2.56 8000 0.1409 0.9675
0.0513 2.59 8100 0.1401 0.9663
0.0083 2.62 8200 0.1340 0.9679
0.0144 2.66 8300 0.1378 0.9671
0.0958 2.69 8400 0.1385 0.9667
0.011 2.72 8500 0.1265 0.9689
0.0022 2.75 8600 0.1268 0.9671
0.0814 2.78 8700 0.1291 0.9695
0.0092 2.82 8800 0.1216 0.9714
0.0178 2.85 8900 0.1156 0.972
0.0082 2.88 9000 0.1070 0.9745
0.0325 2.91 9100 0.1110 0.9744
0.0197 2.94 9200 0.1244 0.972
0.0291 2.98 9300 0.1303 0.9719
0.0012 3.01 9400 0.1283 0.9714
0.0089 3.04 9500 0.1221 0.974
0.0115 3.07 9600 0.1241 0.9737
0.0015 3.1 9700 0.1335 0.971
0.0018 3.14 9800 0.1315 0.9716
0.0004 3.17 9900 0.1127 0.9739
0.0128 3.2 10000 0.1168 0.976
0.0194 3.23 10100 0.1193 0.9749
0.0003 3.26 10200 0.1145 0.9759
0.0056 3.3 10300 0.1231 0.9734
0.0089 3.33 10400 0.1392 0.9723
0.0032 3.36 10500 0.1274 0.9728
0.0003 3.39 10600 0.1236 0.9748
0.0002 3.42 10700 0.1299 0.9737
0.0055 3.46 10800 0.1307 0.9735
0.0008 3.49 10900 0.1348 0.9731
0.0003 3.52 11000 0.1345 0.973
0.0288 3.55 11100 0.1238 0.9759
0.0217 3.58 11200 0.1233 0.9754
0.0012 3.62 11300 0.1203 0.9768
0.0008 3.65 11400 0.1165 0.9768
0.0003 3.68 11500 0.1199 0.9776
0.0028 3.71 11600 0.1258 0.9764
0.0014 3.74 11700 0.1217 0.9766
0.0225 3.78 11800 0.1242 0.9763
0.0002 3.81 11900 0.1214 0.9778
0.0017 3.84 12000 0.1213 0.9775
0.0002 3.87 12100 0.1214 0.9778
0.001 3.9 12200 0.1207 0.9773
0.0008 3.94 12300 0.1190 0.9782
0.0002 3.97 12400 0.1187 0.9784
0.0003 4.0 12500 0.1187 0.9784

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1