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beit-base-patch16-224-pt22k-ft22k-rim_one-new

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4550
  • Accuracy: 0.8767

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.73 2 0.2411 0.9178
No log 1.73 4 0.2182 0.8973
No log 2.73 6 0.3085 0.8973
No log 3.73 8 0.2794 0.8973
0.1392 4.73 10 0.2398 0.9110
0.1392 5.73 12 0.2925 0.8973
0.1392 6.73 14 0.2798 0.9110
0.1392 7.73 16 0.2184 0.9178
0.1392 8.73 18 0.3007 0.9110
0.0416 9.73 20 0.3344 0.9041
0.0416 10.73 22 0.3626 0.9110
0.0416 11.73 24 0.4842 0.8904
0.0416 12.73 26 0.3664 0.8973
0.0416 13.73 28 0.3458 0.9110
0.0263 14.73 30 0.2810 0.9110
0.0263 15.73 32 0.4695 0.8699
0.0263 16.73 34 0.3723 0.9041
0.0263 17.73 36 0.3447 0.9041
0.0263 18.73 38 0.3708 0.8904
0.0264 19.73 40 0.4052 0.9110
0.0264 20.73 42 0.4492 0.9041
0.0264 21.73 44 0.4649 0.8904
0.0264 22.73 46 0.4061 0.9178
0.0264 23.73 48 0.4136 0.9110
0.0139 24.73 50 0.4183 0.8973
0.0139 25.73 52 0.4504 0.8904
0.0139 26.73 54 0.4368 0.8973
0.0139 27.73 56 0.4711 0.9110
0.0139 28.73 58 0.3928 0.9110
0.005 29.73 60 0.4550 0.8767

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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Evaluation results