Single Shot MultiBox Detector training in PyTorch

This example shows how DALI can be used in detection networks, specifically Single Shot Multibox Detector originally published by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg as SSD: Single Shot MultiBox Detector.

Code is based on MLPerf example and has been modified to use DALI.

To run use following command:

python train.py

Requirements

• This example was tested with python3.5.2 and it should work with later versions. It will not work with python2.7 and earlier.

• Download COCO 2017 dataset. You can also use:

  dir=$(pwd)
  mkdir /coco; cd /coco
  curl -O http://images.cocodataset.org/zips/train2017.zip; unzip train2017.zip
  curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
  curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
  cd $dir
  

• Install packages listed below into your python interpreter:

  numpy torch torchvision mlperf_compliance matplotlib Cython pycocotools

Usage

python train.py [-h] [--data DATA] [--epochs EPOCHS] [--batch-size BATCH_SIZE]
                [--seed SEED] [--threshold THRESHOLD] [--iteration ITERATION]
                [--checkpoint CHECKPOINT] [--no-save]
                [--evaluation [EVALUATION [EVALUATION ...]]]

For example, if you have COCO data in /data/coco2017 and wish to train for 80 epochs you could use:

python train.py --data=/data/coco2017 --epochs=80

All arguments with descriptions you can find in table below:

Argument	Description
-h, –help	show this help message and exit
–data DATA, -d DATA	path to test and training data files
–epochs EPOCHS, -e EPOCHS	number of epochs for training
–batch-size BATCH_SIZE, -b BATCH_SIZE	number of examples for each iteration
–seed SEED, -s SEED	manually set random seed for torch
–threshold THRESHOLD, -t THRESHOLD	stop training early at threshold
–iteration ITERATION	iteration to start from
–checkpoint CHECKPOINT	path to model checkpoint file
–no-save	save model checkpoints
–evaluation [EVALUATION [EVALUATION …]]	iterations at which to evaluate