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