One-Shot Learning with Triplet CNNs in Pytorch
The following repository contains code for training Triplet Network in Pytorch
Siamese and Triplet networks make use of a similarity metric with the aim of bringing similar images closer in the embedding space while separating non similar ones.
Popular uses of such networks being -
Paper - Deep metric learning using Triplet network
Install PyTorch
pip install -r requirements.txt
Colab notebook with pretrained weights
python train.py --cuda
This by default will train on the MNIST dataset
python train.py --result_dir results --exp_name MNIST_exp1 --cuda --dataset <manist>/<fmnist>
To create a tSNE visualisation
python tsne.py --ckp <path to model>
The embeddings and the labels are stored in the experiment folder as a pickle file, and you do not have to run the model everytime you create a visualisation. Just pass the saved embeddings as the —pkl parameter
python tsne.py --pkl <path to stored embeddings>
Sample tSNE visualisation on MNIST
Specify the location of the dataset in test.yaml
The directory should have the following structure
+-- root| +-- train| +-- class1| +-- img1.jpg| +-- img2.jpg| +-- img3.jpg| +-- class2| +-- class3| +-- test| +-- class4| +-- class5
python train.py --result_dir results --exp_name VGGFace2_exp1 --cuda --epochs 50 --ckp_freq 5 --dataset vggface2 --num_train_samples 32000 --num_test_samples 5000 --train_log_step 50
Specify the location of the dataset in test.yaml
The directory should have the following structure
+-- root| +-- train| +-- class1| +-- img1.jpg| +-- img2.jpg| +-- img3.jpg| +-- class2| +-- class3| +-- test| +-- class4| +-- class5
python train.py --result_dir results --exp_name Custom_exp1 --cuda --epochs 50 --ckp_freq 5 --dataset custom --num_train_samples 32000 --num_test_samples 5000 --train_log_step 50