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exper6_mesum5

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metadata
license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: exper6_mesum5
    results: []

exper6_mesum5

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8241
  • Accuracy: 0.8036

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: 16
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.9276 0.23 100 3.8550 0.2089
3.0853 0.47 200 3.1106 0.3414
2.604 0.7 300 2.5732 0.4379
2.3183 0.93 400 2.2308 0.4882
1.5326 1.16 500 1.7903 0.5828
1.3367 1.4 600 1.5524 0.6349
1.1544 1.63 700 1.3167 0.6645
1.0788 1.86 800 1.3423 0.6385
0.6762 2.09 900 1.0780 0.7124
0.6483 2.33 1000 1.0090 0.7284
0.6321 2.56 1100 1.0861 0.7024
0.5558 2.79 1200 0.9933 0.7183
0.342 3.02 1300 0.8871 0.7462
0.2964 3.26 1400 0.9330 0.7408
0.1959 3.49 1500 0.9367 0.7343
0.368 3.72 1600 0.8472 0.7550
0.1821 3.95 1700 0.8937 0.7568
0.1851 4.19 1800 0.9546 0.7485
0.1648 4.42 1900 0.9790 0.7355
0.172 4.65 2000 0.8947 0.7627
0.0928 4.88 2100 1.0093 0.7462
0.0699 5.12 2200 0.8374 0.7639
0.0988 5.35 2300 0.9189 0.7645
0.0822 5.58 2400 0.9512 0.7580
0.1223 5.81 2500 1.0809 0.7349
0.0509 6.05 2600 0.9297 0.7769
0.0511 6.28 2700 0.8981 0.7822
0.0596 6.51 2800 0.9468 0.7704
0.0494 6.74 2900 0.9045 0.7870
0.0643 6.98 3000 1.1559 0.7391
0.0158 7.21 3100 0.8450 0.7899
0.0129 7.44 3200 0.8241 0.8036
0.0441 7.67 3300 0.9679 0.7751
0.0697 7.91 3400 1.0387 0.7751
0.0084 8.14 3500 0.9441 0.7947
0.0182 8.37 3600 0.8967 0.7994
0.0042 8.6 3700 0.8750 0.8041
0.0028 8.84 3800 0.9349 0.8041
0.0053 9.07 3900 0.9403 0.7982
0.0266 9.3 4000 0.9966 0.7959
0.0022 9.53 4100 0.9472 0.8018
0.0018 9.77 4200 0.8717 0.8136
0.0018 10.0 4300 0.8964 0.8083
0.0046 10.23 4400 0.8623 0.8160
0.0037 10.47 4500 0.8762 0.8172
0.0013 10.7 4600 0.9028 0.8142
0.0013 10.93 4700 0.9084 0.8178
0.0013 11.16 4800 0.8733 0.8213
0.001 11.4 4900 0.8823 0.8207
0.0009 11.63 5000 0.8769 0.8213
0.0282 11.86 5100 0.8791 0.8219
0.001 12.09 5200 0.8673 0.8249
0.0016 12.33 5300 0.8633 0.8225
0.0008 12.56 5400 0.8766 0.8195
0.0008 12.79 5500 0.8743 0.8225
0.0008 13.02 5600 0.8752 0.8231
0.0008 13.26 5700 0.8676 0.8237
0.0007 13.49 5800 0.8677 0.8237
0.0008 13.72 5900 0.8703 0.8237
0.0007 13.95 6000 0.8725 0.8237
0.0006 14.19 6100 0.8741 0.8231
0.0006 14.42 6200 0.8758 0.8237
0.0008 14.65 6300 0.8746 0.8243
0.0007 14.88 6400 0.8759 0.8243
0.0007 15.12 6500 0.8803 0.8231
0.0007 15.35 6600 0.8808 0.8237
0.0007 15.58 6700 0.8798 0.8243
0.0007 15.81 6800 0.8805 0.8243

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1