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This model is a fine-tuned version of facebook/dinov2-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0742
  • Precision: 0.9306
  • Recall: 0.8969
  • F1: 0.9135

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: 1e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 8
  • 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: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1
0.5948 0.98 39 0.4487 0.1103 0.0658 0.0824
0.2211 1.98 79 0.2079 0.8179 0.5614 0.6658
0.1241 2.98 119 0.1378 0.8880 0.7390 0.8067
0.0954 3.99 159 0.1117 0.8916 0.8114 0.8496
0.0801 4.99 199 0.0980 0.9167 0.8322 0.8724
0.0716 5.99 239 0.0875 0.9245 0.8596 0.8909
0.0641 7.0 279 0.0871 0.9231 0.8421 0.8807
0.0615 8.0 319 0.0804 0.9318 0.8838 0.9071
0.056 8.98 358 0.0793 0.9257 0.8882 0.9065
0.0541 9.98 398 0.0761 0.9335 0.8925 0.9126
0.0532 10.98 438 0.0767 0.9339 0.8827 0.9076
0.053 11.99 478 0.0758 0.9312 0.8904 0.9103
0.048 12.99 518 0.0743 0.9324 0.8925 0.9120
0.047 13.99 558 0.0750 0.9303 0.8925 0.9110
0.0476 14.67 585 0.0742 0.9306 0.8969 0.9135

Framework versions

  • Transformers 4.37.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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