Single Shot MultiBox Detector training in PaddlePaddle¶

This demo shows how to use DALI with PaddlePaddle for training Single Shot Multibox Detector (SSD: Single Shot MultiBox Detector).

The model is designed to train on 8 GPUs with a mini-batch size of 8 per GPU, to train on all GPUs of the system, simply run:

python train.py -b 8 [path to coco dataset]

Requirements¶

Download and extract the COCO2017 dataset.

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip train2017.zip
unzip val2017.zip
unzip annotations_trainval2017.zip

Install the following python packages via pip or other means.
- PaddlePaddle (1.6 or above)
- Nvidia DALI

Usage¶

usage: train.py [-h] [-j N] [-b N] [--lr LR] [--momentum M] [--weight-decay W]
                [--print-freq N] [--ckpt-freq N]
                DIR

Paddle Single Shot MultiBox Detector Training

positional arguments:
  DIR                   path to dataset

optional arguments:
  -h, --help            show this help message and exit
  -j N, --num_threads N
                        number of threads (default: 4)
  -b N, --batch-size N  mini-batch size (default: 8)
  --lr LR, --learning-rate LR
                        initial learning rate
  --momentum M          momentum
  --weight-decay W, --wd W
                        weight decay (default: 1e-4)
  --print-freq N, -p N  print frequency (default: 10)
  --ckpt-freq N, -c N   checkpoint frequency (default: 5000)