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vit-base-patch16-224-dmae-va-da2-40

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-dmae-va-da2-40
    results: []
datasets:
  - Augusto777/dmae-ve-da2

vit-base-patch16-224-dmae-va-da2-40

This model is a fine-tuned version of google/vit-base-patch16-224 on Augusto777/dmae-ve-da2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2660
  • Accuracy: 0.9655

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: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.94 4 1.2931 0.4310
No log 1.88 8 1.2024 0.5517
1.2651 2.82 12 0.9896 0.6552
1.2651 4.0 17 0.7972 0.7241
1.2651 4.94 21 0.7336 0.6552
0.7523 5.88 25 0.5781 0.8103
0.7523 6.82 29 0.4912 0.8793
0.7523 8.0 34 0.4112 0.9138
0.4209 8.94 38 0.3383 0.9138
0.4209 9.88 42 0.3129 0.9483
0.4209 10.82 46 0.2660 0.9655
0.2647 12.0 51 0.3184 0.9310
0.2647 12.94 55 0.2871 0.9310
0.2647 13.88 59 0.2766 0.9138
0.1743 14.82 63 0.2727 0.8966
0.1743 16.0 68 0.2282 0.9310
0.1511 16.94 72 0.2892 0.8966
0.1511 17.88 76 0.2482 0.8966
0.1511 18.82 80 0.2363 0.9310
0.1253 20.0 85 0.1622 0.9483
0.1253 20.94 89 0.1753 0.9483
0.1253 21.88 93 0.1593 0.9655
0.087 22.82 97 0.1334 0.9483
0.087 24.0 102 0.1088 0.9483
0.087 24.94 106 0.1130 0.9483
0.0856 25.88 110 0.1459 0.9138
0.0856 26.82 114 0.1445 0.9655
0.0856 28.0 119 0.1234 0.9655
0.081 28.94 123 0.1224 0.9483
0.081 29.88 127 0.1303 0.9483
0.081 30.82 131 0.1372 0.9483
0.0554 32.0 136 0.1421 0.9483
0.0554 32.94 140 0.1307 0.9483
0.0783 33.88 144 0.1244 0.9483
0.0783 34.82 148 0.1195 0.9483
0.0783 36.0 153 0.1171 0.9483
0.0646 36.94 157 0.1165 0.9483
0.0646 37.65 160 0.1163 0.9483

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1