Skip to content

Latest commit

 

History

History
32 lines (26 loc) · 1.16 KB

README.md

File metadata and controls

32 lines (26 loc) · 1.16 KB
Raw

One Shot Image Recognition

An implementation of the paper Siamese Neural Networks for One-shot Image Recognition. The model architecture is as below (taken from the paper):

With the only difference being same learning rate for all parameters, instead of layer wise learning rate as in the paper. With this, achieves 93% accuracy with 30k pairs.

Additional features:

  • Training loss and validation accuracy are logged via TensorBoard.
  • Best model as per validation accuracy saved at the end of training.

Requirements (Python 3.6+):

  • PyTorch
  • Augmentor

Usage:

  • Download the Omniglot 'background' and 'evaluation' zip files from here.
  • Run data_prep.py to split the dataset into train-val-test:
python data_prep.py <DATA_DIR>

where DATA_DIR is the directory containing both the unzipped files.

  • Run train.py to train the model:
python train.py <DATA_DIR>
  • Hyperparameters such as learning rate, number of pairs for training, augmentation etc. can be changed in train.py .