metadata
library_name: keras
tags:
- image-classification
Model description
Metric learning aims to measure the similarity among data samples and to learn embedding models. In the context of metric learning 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.
These models once trained can produce embeddings for downstream systems where such similarity is useful.
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
The following hyperparameters were used during training:
- learning_rate: 0.001
- training_precision: float32
- num_epochs: 20
- Optimizer Info:
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
