A higher performance PyTorch implementation of Single-Shot Refinement Neural Network for Object Detection. The official and original Caffe code can be found here.
Table of Contents
mAP (Single Scale Test)
|Arch||Paper||Caffe Version||Our PyTorch Version|
- Install PyTorch by selecting your environment on the website and running the appropriate command.
- Note: You should use at least PyTorch0.4.0
- Clone this repository.
- Note: We currently only support Python 3+.
- Then download the dataset by following the instructions below.
- We now support Visdom for real-time loss visualization during training!
- To use Visdom in the browser:
# First install Python server and client pip install visdom # Start the server (probably in a screen or tmux) python -m visdom.server
- Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
- Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.
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
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
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>
- First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
- By default, we assume you have downloaded the file in the
mkdir weights cd weights wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
- To train RefineDet320 or RefineDet512 using the train scripts
train_refinedet512.sh. You can manually change them as you want.
- 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
To evaluate a trained network:
You can specify the parameters listed in the
eval_refinedet.py file by flagging them or manually changing them.
We have accumulated the following to-do list, which we hope to complete in the near future
- Still to come:
- Support for multi-scale testing