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)
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)