| library_name: tf-keras | |
| tags: | |
| - computer-vision | |
| - image-classification | |
| ## Model description | |
| This repo contains the trained model Self-supervised contrastive learning with SimSiam on CIFAR-10 Dataset. | |
| Keras link: https://keras.io/examples/vision/simsiam/ | |
| Full credits to https://twitter.com/RisingSayak | |
| ## Intended uses & limitations | |
| The trained model can be used as a learned representation for downstream tasks like image classification. | |
| ## Training and evaluation data | |
| The dataset we are using here is called CIFAR-100. The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. | |
| Two particular augmentation transforms that seem to matter the most are: | |
| - Random resized crops | |
| - Color distortions | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| | name | learning_rate | decay | momentum | nesterov | training_precision | | |
| |----|-------------|-----|--------|--------|------------------| | |
| |SGD|{'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 0.03, 'decay_steps': 3900, 'alpha': 0.0, 'name': None}}|0.0|0.8999999761581421|False|float32| | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |