/SSD.Pytorch1.0

SSD impliments use Pytroch1.0

Primary LanguagePythonApache License 2.0Apache-2.0

SSD.Pytorch1.0

Note: we clone reposiable from ssd.pytorch , and do some modify :

TODO

  • Still to come:
    • Support for the MS COCO dataset
    • Support pytroch1.0
    • save training some data: loss ,ac
    • Do evaluate
    • Support for SSD512 training and testing
    • Support for training on custom datasets c

SSD: Single Shot MultiBox Object Detector, in PyTorch

A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here.

Installation

  • Install PyTorch by selecting your environment on the website and running the appropriate command.
  • Clone this repository.
    • Note: We currently only support Python 3+.
  • Then download the dataset by following the instructions below.
  • Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

Datasets

To make things easy, we provide bash scripts to handle the dataset downloads and setup for you. We also provide simple dataset loaders that inherit torch.utils.data.Dataset, making them fully compatible with the torchvision.datasets API.

COCO

Microsoft COCO: Common Objects in Context

Download COCO 2014
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh

VOC Dataset

PASCAL VOC: Visual Object Classes

Download VOC2007 trainval & test
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>
Download VOC2012 trainval
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

Training SSD

mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
  • To train SSD using the train script simply specify the parameters listed in train.py as a flag or manually change them.
python train.py
  • Note:
    • For training, an NVIDIA GPU is strongly recommended for speed.
    • For instructions on Visdom usage/installation, see the Installation section.
    • You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see train.py for options)

Authors

Note: Unfortunately, this is just a hobby of ours and not a full-time job, so we'll do our best to keep things up to date, but no guarantees. That being said, thanks to everyone for your continued help and feedback as it is really appreciated. We will try to address everything as soon as possible.

References