To train, run

  python train.py --config path/to/config/file [--gpus gpu_id] [--debug]

Arguments:

  --config: path to configuration file
  --gpus: gpu id to be used
  --debug: to save the weights or not

For example:

  python train.py --config configs/train/debug_ircad.yaml --gpus 0 --debug

Modify the default configuration file (YAML format) to suit your need. The mechanism behind is exactly the same as creating an object of a class noted in name, with keyword arguments noted in args.

All the result will be stored in the runs folder in separate subfolders, one for each run. The result consists of the log file for Tensorboard, the network pretrained models (best metrics, best loss, and the latest iteration).

This project uses Tensorboard to plot training graph. To see it, run

  tensorboard --logdir=logs

and access using the announced port (default is 6006, e.g http://localhost:6006).

The .pth file contains a dictionary:

  {
      'epoch':                the epoch of the training where the weight is saved
      'model_state_dict':     model state dict (use model.load_state_dict to load)
      'optimizer_state_dict': optimizer state dict (use opt.load_state_dict to load)
      'config':               full configuration of that run
  }

This repository heavily borrows the code from https://github.com/adambielski/siamese-triplet with modification to fit with the template. Specifically, things taken from the repository linked above include:

• Siamese/Triplet Network
• (Online) Contrastive Loss/Triplet Loss
• Balanced Batch Sampler
• All/Random/Hard negative mining of Pairs/Triplets

Mean Average Precision (mAP) is borrowed and modified from https://github.com/CoinCheung/triplet-reid-pytorch.

Re-ranking is taken from https://github.com/zhunzhong07/person-re-ranking.

Visualization is taken from AIC Challenge 2020 (https://www.aicitychallenge.org/).