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This model is a fine-tuned version of facebook/dinov2-base-imagenet1k-1-layer on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0747
  • Accuracy: 0.4865

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: 10
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 20
  • 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 1.0 2 1.2201 0.3378
No log 2.0 4 1.2243 0.2568
No log 3.0 6 1.2672 0.2703
No log 4.0 8 1.2501 0.2297
1.2006 5.0 10 1.1975 0.2973
1.2006 6.0 12 1.1270 0.3919
1.2006 7.0 14 1.0999 0.3243
1.2006 8.0 16 1.1497 0.3649
1.2006 9.0 18 1.1006 0.3108
1.1058 10.0 20 1.1271 0.3514
1.1058 11.0 22 1.1273 0.3784
1.1058 12.0 24 1.1639 0.2838
1.1058 13.0 26 1.1421 0.4054
1.1058 14.0 28 1.1190 0.3514
1.0489 15.0 30 1.1735 0.3243
1.0489 16.0 32 1.1422 0.3378
1.0489 17.0 34 1.1414 0.3649
1.0489 18.0 36 1.1033 0.4189
1.0489 19.0 38 1.0747 0.3919
0.9717 20.0 40 1.0952 0.3919
0.9717 21.0 42 1.1063 0.3784
0.9717 22.0 44 1.0822 0.3649
0.9717 23.0 46 1.0768 0.3784
0.9717 24.0 48 1.0753 0.4595
0.9816 25.0 50 1.0531 0.4054
0.9816 26.0 52 1.0624 0.4189
0.9816 27.0 54 1.0690 0.4459
0.9816 28.0 56 1.1392 0.3514
0.9816 29.0 58 1.0696 0.4054
0.9576 30.0 60 1.0747 0.4865

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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