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Xanadu00/galaxy_classifier_mobilevit

This model is a fine-tuned version of apple/mobilevit-small on Galaxy10 DECals dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.3550
  • Train Accuracy: 0.8797
  • Validation Loss: 0.5428
  • Validation Accuracy: 0.8326
  • Epoch: 14

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Dataset Fields

The dataset has the following fields (also called "features"):

{
  "image": "Image(decode=True, id=None)",
  "target": "ClassLabel(names=['Barred Spiral', 'Cigar Round Smooth', 'Distributed', 'Edge-on with Bulge', 'Edge-on without Bulge', 'In-between Round Smooth', 'Merging', 'Round Smooth', 'Unbarred Loss Spiral', 'Unbarred Tight Spiral'], id=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])"
}

Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

Split name Num samples
train 14188
valid 3548

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'AdamW', 'weight_decay': 0.004, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.002, 'decay_steps': 10000, 'end_learning_rate': 2e-05, 'power': 0.5, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Train Accuracy Validation Loss Validation Accuracy Epoch
0.9179 0.6833 0.7849 0.7342 0
0.6763 0.7702 0.7576 0.7556 1
0.6042 0.7912 0.5977 0.7909 2
0.5604 0.8063 0.5499 0.8117 3
0.5176 0.8213 0.5738 0.8114 4
0.4944 0.8353 0.5317 0.8176 5
0.4735 0.8368 0.6051 0.7914 6
0.4427 0.8483 0.5357 0.8275 7
0.4430 0.8480 0.5327 0.8250 8
0.4127 0.8596 0.4947 0.8362 9
0.4017 0.8603 0.5378 0.8162 10
0.3840 0.8694 0.5070 0.8326 11
0.3677 0.8722 0.4875 0.8295 12
0.3606 0.8778 0.5071 0.8360 13
0.3550 0.8797 0.5428 0.8326 14

Framework versions

  • Transformers 4.30.2
  • TensorFlow 2.12.0
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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