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swinv2-tiny-patch4-window8-256-dmae-va-U5-42B

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swinv2-tiny-patch4-window8-256-dmae-va-U5-42B / README.md
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metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-dmae-va-U5-42B
    results: []

swinv2-tiny-patch4-window8-256-dmae-va-U5-42B

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8386
  • Accuracy: 0.65

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: 4e-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: 42

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.9 7 6.1748 0.1167
5.327 1.94 15 6.0660 0.1167
5.327 2.97 23 5.4902 0.1167
5.0963 4.0 31 4.2768 0.1167
3.9193 4.9 38 3.0013 0.1167
3.9193 5.94 46 1.9289 0.1167
2.2222 6.97 54 1.3857 0.1167
1.4465 8.0 62 1.3423 0.4333
1.4465 8.9 69 1.2786 0.45
1.3709 9.94 77 1.2654 0.4667
1.3511 10.97 85 1.2605 0.4667
1.3511 12.0 93 1.2184 0.4667
1.2749 12.9 100 1.2894 0.5
1.222 13.94 108 1.2072 0.5167
1.222 14.97 116 1.1749 0.5167
1.1668 16.0 124 1.1988 0.5167
1.1668 16.9 131 1.2306 0.5167
1.101 17.94 139 1.1432 0.5333
1.029 18.97 147 1.0208 0.55
1.029 20.0 155 0.9577 0.6167
0.9403 20.9 162 0.9479 0.5
0.8887 21.94 170 0.8910 0.5833
0.8887 22.97 178 0.9442 0.5333
0.8506 24.0 186 0.8923 0.6
0.8064 24.9 193 0.8973 0.6
0.8064 25.94 201 0.9079 0.55
0.7434 26.97 209 0.8386 0.65
0.7404 28.0 217 0.8645 0.6167
0.7404 28.9 224 0.8599 0.5667
0.7215 29.94 232 0.8420 0.65
0.6743 30.97 240 0.8553 0.5667
0.6743 32.0 248 0.8355 0.6167
0.6767 32.9 255 0.8694 0.5833
0.6767 33.94 263 0.8559 0.65
0.6606 34.97 271 0.8351 0.6167
0.6488 36.0 279 0.8287 0.6333
0.6488 36.9 286 0.8377 0.6167
0.6544 37.94 294 0.8406 0.6

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0