Prot10 / convnextv2-base-1k-224-for-pre_evaluation

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Total runs: 11
24-hour runs: 0
7-day runs: 0
30-day runs: -6
Model's Last Updated: September 12 2023
image-classification

Introduction of convnextv2-base-1k-224-for-pre_evaluation

Model Details of convnextv2-base-1k-224-for-pre_evaluation

convnextv2-base-1k-224-for-pre_evaluation

This model is a fine-tuned version of facebook/convnextv2-base-1k-224 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3599
  • Accuracy: 0.4190
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
1.6 1.0 16 1.5316 0.2961
1.5084 2.0 32 1.5061 0.2849
1.5134 3.0 48 1.4968 0.3240
1.4663 4.0 64 1.4607 0.3352
1.4046 5.0 80 1.4509 0.3268
1.4085 6.0 96 1.4423 0.3883
1.3443 7.0 112 1.4005 0.4022
1.3025 8.0 128 1.3599 0.4190
1.2627 9.0 144 1.3638 0.3911
1.2099 10.0 160 1.4058 0.3492
1.2086 11.0 176 1.4431 0.3408
1.1393 12.0 192 1.4143 0.3492
1.1039 13.0 208 1.4305 0.3883
1.0551 14.0 224 1.5203 0.3520
1.0368 15.0 240 1.5117 0.3324
0.9753 16.0 256 1.4545 0.3771
0.938 17.0 272 1.5396 0.3352
0.899 18.0 288 1.5770 0.3408
0.8629 19.0 304 1.7106 0.3128
0.8674 20.0 320 1.5864 0.3352
0.7789 21.0 336 1.6129 0.3408
0.7426 22.0 352 1.6353 0.3603
0.7677 23.0 368 1.6793 0.3464
0.7172 24.0 384 1.6759 0.3575
0.6809 25.0 400 1.7013 0.3659
0.6619 26.0 416 1.7108 0.3631
0.6656 27.0 432 1.7327 0.3715
0.6258 28.0 448 1.7378 0.3547
0.6173 29.0 464 1.7461 0.3603
0.6214 30.0 480 1.7475 0.3520
Framework versions
  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3

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Updated:June 05 2023