Xanadu00/galaxy-classification-mobilevit

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