Siamese Network Example

Siamese network for image similarity estimation. The network is composed of two identical networks, one for each input. The output of each network is concatenated and passed to a linear layer. The output of the linear layer passed through a sigmoid function. FaceNet is a variant of the Siamese network. This implementation varies from FaceNet as we use the ResNet-18 model from Deep Residual Learning for Image Recognition as our feature extractor. In addition, we aren't using TripletLoss as the MNIST dataset is simple, so BCELoss can do the trick.

Usage

Install the required dependencies:

pip install -r requirements.txt

To run the example, execute:

python main.py
# CUDA_VISIBLE_DEVICES=2 python main.py  # to specify GPU id to ex. 2

If a hardware accelerator device is detected, the example will execute on the accelerator; otherwise, it will run on the CPU.

To force execution on the CPU, use --no-accel command line argument:

python main.py --no-accel

Optionally, you can add the following arguments to customize your execution.

--batch-size            input batch size for training (default: 64)
--test-batch-size       input batch size for testing (default: 1000)
--epochs                number of epochs to train (default: 14)
--lr                    learning rate (default: 1.0)
--gamma                 learning rate step gamma (default: 0.7)
--no-accel              disables accelerator
--dry-run               quickly check a single pass
--seed                  random seed (default: 1)
--log-interval          how many batches to wait before logging training status
--save-model            Saving the current Model