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sdss-cnn

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cnn
Generated from Trainer
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metadata
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: sdss-cnn
    results: []

sdss-cnn

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

  • Loss: 0.1573
  • Accuracy: 0.9505

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: 0.0001
  • train_batch_size: 100
  • eval_batch_size: 100
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 80 0.4954 0.8635
No log 2.0 160 0.2788 0.9055
No log 3.0 240 0.2239 0.9085
No log 4.0 320 0.1991 0.9325
No log 5.0 400 0.1954 0.94
No log 6.0 480 0.1854 0.9445
0.3543 7.0 560 0.1891 0.9375
0.3543 8.0 640 0.1777 0.943
0.3543 9.0 720 0.1780 0.9415
0.3543 10.0 800 0.1804 0.942
0.3543 11.0 880 0.1734 0.9475
0.3543 12.0 960 0.1689 0.947
0.2022 13.0 1040 0.1698 0.9445
0.2022 14.0 1120 0.1689 0.9405
0.2022 15.0 1200 0.1650 0.9475
0.2022 16.0 1280 0.1755 0.934
0.2022 17.0 1360 0.1635 0.944
0.2022 18.0 1440 0.1711 0.942
0.1836 19.0 1520 0.1604 0.9485
0.1836 20.0 1600 0.1595 0.95
0.1836 21.0 1680 0.1613 0.9475
0.1836 22.0 1760 0.1579 0.949
0.1836 23.0 1840 0.1593 0.946
0.1836 24.0 1920 0.1579 0.945
0.167 25.0 2000 0.1584 0.9495
0.167 26.0 2080 0.1573 0.9505
0.167 27.0 2160 0.1596 0.945
0.167 28.0 2240 0.1599 0.9435
0.167 29.0 2320 0.1565 0.9485
0.167 30.0 2400 0.1582 0.946
0.167 31.0 2480 0.1563 0.95
0.1568 32.0 2560 0.1563 0.95
0.1568 33.0 2640 0.1573 0.9495
0.1568 34.0 2720 0.1564 0.9465
0.1568 35.0 2800 0.1557 0.95
0.1568 36.0 2880 0.1554 0.949
0.1568 37.0 2960 0.1562 0.948
0.1515 38.0 3040 0.1555 0.948
0.1515 39.0 3120 0.1557 0.95
0.1515 40.0 3200 0.1559 0.9485

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3