keras-io/cifar10_metric_learning

Model description

Metric learning aims to measure the similarity among data samples and to learn embedding models. The motivation is to embed inputs in an embedding space such that similar images are close together in that space while dissimilar ones are far away.

The model in this repo is an example which demonstrates the capabilities of metric learning to create embeddings. These embeddings are then used to perform Image Similarity Search.

Full credits to Mat Kelcey for this work.

Intended uses & limitations

More information needed

Training and evaluation data

Trained and evaluated on CIFAR-10 dataset.

Training procedure

Training hyperparameters

name	learning_rate	decay	beta_1	beta_2	epsilon	amsgrad	training_precision
Adam	0.0010000000474974513	0.0	0.8999999761581421	0.9990000128746033	1e-07	False	float32

Training Metrics

Epochs	Train Loss
1	2.248
2	2.11
3	2.042
4	1.998
5	1.957
6	1.929
7	1.897
8	1.879
9	1.844
10	1.807
11	1.799
12	1.761
13	1.762
14	1.735
15	1.713
16	1.687
17	1.669
18	1.646
19	1.633
20	1.619

Model Plot

View Model Plot