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exper_batch_8_e8

Image Classification
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vit
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metadata
license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: exper_batch_8_e8
    results: []

exper_batch_8_e8

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4608
  • Accuracy: 0.9052

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Apex, opt level O1

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.2202 0.08 100 4.1245 0.1237
3.467 0.16 200 3.5622 0.2143
3.3469 0.23 300 3.1688 0.2675
2.8086 0.31 400 2.8965 0.3034
2.6291 0.39 500 2.5858 0.4025
2.2382 0.47 600 2.2908 0.4133
1.9259 0.55 700 2.2007 0.4676
1.8088 0.63 800 2.0419 0.4742
1.9462 0.7 900 1.6793 0.5578
1.5392 0.78 1000 1.5460 0.6079
1.561 0.86 1100 1.5793 0.5690
1.2135 0.94 1200 1.4663 0.5929
1.0725 1.02 1300 1.2974 0.6534
0.8696 1.1 1400 1.2406 0.6569
0.8758 1.17 1500 1.2127 0.6623
1.1737 1.25 1600 1.2243 0.6550
0.8242 1.33 1700 1.1371 0.6735
1.0141 1.41 1800 1.0536 0.7024
0.9855 1.49 1900 0.9885 0.7205
0.805 1.57 2000 0.9048 0.7479
0.7207 1.64 2100 0.8842 0.7490
0.7101 1.72 2200 0.8954 0.7436
0.5946 1.8 2300 0.9174 0.7386
0.6937 1.88 2400 0.7818 0.7760
0.5593 1.96 2500 0.7449 0.7934
0.4139 2.04 2600 0.7787 0.7830
0.2929 2.11 2700 0.7122 0.7945
0.4159 2.19 2800 0.7446 0.7907
0.4079 2.27 2900 0.7354 0.7938
0.516 2.35 3000 0.7499 0.8007
0.2728 2.43 3100 0.6851 0.8061
0.4159 2.51 3200 0.7258 0.7999
0.3396 2.58 3300 0.7455 0.7972
0.1918 2.66 3400 0.6793 0.8119
0.1228 2.74 3500 0.6696 0.8134
0.2671 2.82 3600 0.6306 0.8285
0.4986 2.9 3700 0.6111 0.8296
0.3699 2.98 3800 0.5600 0.8508
0.0444 3.05 3900 0.6021 0.8331
0.1489 3.13 4000 0.5599 0.8516
0.15 3.21 4100 0.6377 0.8365
0.2535 3.29 4200 0.5752 0.8543
0.2679 3.37 4300 0.5677 0.8608
0.0989 3.45 4400 0.6325 0.8396
0.0825 3.52 4500 0.5979 0.8524
0.0427 3.6 4600 0.5903 0.8516
0.1806 3.68 4700 0.5323 0.8628
0.2672 3.76 4800 0.5688 0.8604
0.2674 3.84 4900 0.5369 0.8635
0.2185 3.92 5000 0.4743 0.8820
0.2195 3.99 5100 0.5340 0.8709
0.0049 4.07 5200 0.5883 0.8608
0.0204 4.15 5300 0.6102 0.8539
0.0652 4.23 5400 0.5659 0.8670
0.028 4.31 5500 0.4916 0.8840
0.0423 4.39 5600 0.5706 0.8736
0.0087 4.46 5700 0.5653 0.8697
0.0964 4.54 5800 0.5423 0.8755
0.0841 4.62 5900 0.5160 0.8743
0.0945 4.7 6000 0.5532 0.8697
0.0311 4.78 6100 0.4947 0.8867
0.0423 4.86 6200 0.5063 0.8843
0.1348 4.93 6300 0.5619 0.8743
0.049 5.01 6400 0.5800 0.8732
0.0053 5.09 6500 0.5499 0.8770
0.0234 5.17 6600 0.5102 0.8874
0.0192 5.25 6700 0.5447 0.8836
0.0029 5.32 6800 0.4787 0.8936
0.0249 5.4 6900 0.5232 0.8870
0.0671 5.48 7000 0.4766 0.8975
0.0056 5.56 7100 0.5136 0.8894
0.003 5.64 7200 0.5085 0.8882
0.0015 5.72 7300 0.4832 0.8971
0.0014 5.79 7400 0.4648 0.8998
0.0065 5.87 7500 0.4739 0.8978
0.0011 5.95 7600 0.5349 0.8867
0.0021 6.03 7700 0.5460 0.8847
0.0012 6.11 7800 0.5309 0.8890
0.0011 6.19 7900 0.4852 0.8998
0.0093 6.26 8000 0.4751 0.8998
0.003 6.34 8100 0.4934 0.8963
0.0027 6.42 8200 0.4882 0.9029
0.0009 6.5 8300 0.4806 0.9021
0.0009 6.58 8400 0.4974 0.9029
0.0009 6.66 8500 0.4748 0.9075
0.0008 6.73 8600 0.4723 0.9094
0.001 6.81 8700 0.4692 0.9098
0.0007 6.89 8800 0.4726 0.9075
0.0011 6.97 8900 0.4686 0.9067
0.0006 7.05 9000 0.4653 0.9056
0.0006 7.13 9100 0.4755 0.9029
0.0007 7.2 9200 0.4633 0.9036
0.0067 7.28 9300 0.4611 0.9036
0.0007 7.36 9400 0.4608 0.9052
0.0007 7.44 9500 0.4623 0.9044
0.0005 7.52 9600 0.4621 0.9056
0.0005 7.6 9700 0.4615 0.9056
0.0005 7.67 9800 0.4612 0.9059
0.0005 7.75 9900 0.4626 0.9075
0.0004 7.83 10000 0.4626 0.9075
0.0005 7.91 10100 0.4626 0.9075
0.0006 7.99 10200 0.4626 0.9079

Framework versions

  • Transformers 4.19.4
  • Pytorch 1.5.1
  • Datasets 2.3.2
  • Tokenizers 0.12.1