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levit-128-finetuned-flower

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levit-128-finetuned-flower

This model is a fine-tuned version of facebook/levit-128 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1807
  • Accuracy: 0.9506
  • Precision: 0.9510
  • Recall: 0.9506
  • F1: 0.9506

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: 0.005
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.6679 1.0 40 0.6957 0.8076 0.8492 0.8076 0.8060
0.7188 2.0 80 0.7094 0.7822 0.7997 0.7822 0.7789
0.7277 3.0 120 0.7803 0.7477 0.7912 0.7477 0.7480
0.561 4.0 160 0.5489 0.8352 0.8462 0.8352 0.8292
0.4958 5.0 200 0.4067 0.8770 0.8852 0.8770 0.8766
0.4681 6.0 240 0.4801 0.8457 0.8570 0.8457 0.8423
0.368 7.0 280 0.4348 0.8617 0.8697 0.8617 0.8618
0.355 8.0 320 0.3401 0.8926 0.8971 0.8926 0.8924
0.3164 9.0 360 0.3510 0.8871 0.8935 0.8871 0.8871
0.2972 10.0 400 0.2877 0.9140 0.9159 0.9140 0.9133
0.2639 11.0 440 0.2588 0.9245 0.9246 0.9245 0.9233
0.264 12.0 480 0.2811 0.9096 0.9155 0.9096 0.9097
0.2082 13.0 520 0.2368 0.9238 0.9244 0.9238 0.9225
0.1506 14.0 560 0.2552 0.9205 0.9244 0.9205 0.9200
0.179 15.0 600 0.2133 0.9401 0.9421 0.9401 0.9399
0.1388 16.0 640 0.2170 0.9376 0.9388 0.9376 0.9377
0.116 17.0 680 0.1817 0.9466 0.9468 0.9466 0.9464
0.0976 18.0 720 0.1915 0.9470 0.9477 0.9470 0.9473
0.0806 19.0 760 0.1876 0.9492 0.9501 0.9492 0.9493
0.0911 20.0 800 0.1807 0.9506 0.9510 0.9506 0.9506

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.0.1
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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Evaluation results

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