readme: ---
  tags: 
  - computer-vision
  - image-classification
  
  library_name: keras
  ---
  
  ## 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>
  
  ![Model Image](./model.png)
  
  </details>