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---
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](https://twitter.com/mat_kelcey) for this work.
## Intended uses & limitations
More information needed
## Training and evaluation data
Trained and evaluated on [CIFAR-10](https://keras.io/api/datasets/cifar10/) 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
<details>
<summary>View Model Plot</summary>
</details> |