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 NVIDIA Deep Learning Examples and has been modified to use full DALI pipeline, it can be found in docs/examples/use_cases/pytorch/single_stage_detector/

To run training on 8 GPUs using half-precision with COCO 2017 dataset under /coco use following command:

torchrun --nproc_per_node=8 ./ --warmup 300 --bs 64 --fp16 --data /coco/


  • 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:

    mkdir /coco; cd /coco
    curl -O; unzip
    curl -O; unzip
    curl -O; unzip
    cd $dir
  • Install packages listed below into your python interpreter:

    numpy torch torchvision mlperf_compliance matplotlib Cython pycocotools


usage: [-h] --data DATA [--epochs EPOCHS] [--batch-size BATCH_SIZE]
             [--eval-batch-size EVAL_BATCH_SIZE] [--seed SEED]
             [--evaluation [EVALUATION [EVALUATION ...]]]
             [--multistep [MULTISTEP [MULTISTEP ...]]] [--target TARGET]
             [--learning-rate LEARNING_RATE] [--momentum MOMENTUM]
             [--weight-decay WEIGHT_DECAY] [--warmup WARMUP]
             [--backbone {resnet18,resnet34,resnet50,resnet101,resnet152}]
             [--num-workers NUM_WORKERS] [--fp16-mode {off,static,amp}]
             [--data_pipeline {dali,no_dali}]

All arguments with descriptions you can find in table below:



-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


epochs at which to evaluate

–multistep [MULTISTEP [MULTISTEP …]]

epochs at which to decay learning rate

–learning-rate LEARNING_RATE

learning rate

–momentum MOMENTUM

momentum argument for SGD optimizer

–weight-decay WEIGHT_DECAY

weight decay value

–warmup WARMUP

number of warmup iterations

–num-workers NUM_WORKERS

number of worker threads


enables half precision mode


target mAP to assert against at the end

–data_pipeline {dali,no_dali}

data pipeline to use for training