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.

UPDATE: We have just added support for MS COCO! Check it out below.

• Max de Groot
• Ellis Brown

Note: Unfortunately, this is just a hobby for us 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.

• Installation
• Datasets
• Train
• Evaluate
• Performance
• Demos
• Future Work
• Reference

• 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.
• 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.
• UPDATE: We have switched from PIL Image support to cv2. The plan is to create a branch that uses PIL as well.

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.

Microsoft COCO: Common Objects in Context

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

PASCAL VOC: Visual Object Classes

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

# 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 ssd.pytorch/weights dir:

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.
  • Currently we only support training on v2 (the newest version).
  • 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)

To evaluate a trained network:

python eval.py

You can specify the parameters listed in the eval.py file by flagging them or manually changing them.

GTX 1060: ~45.45 FPS

• We are trying to provide PyTorch state_dicts (dict of weight tensors) of the latest SSD model definitions trained on different datasets.
• Currently, we provide the following PyTorch models:
  • SSD300 v2 trained on VOC0712 (newest PyTorch version)
    • https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
  • SSD300 v2 trained on VOC0712 (original Caffe version)
    • https://s3.amazonaws.com/amdegroot-models/ssd_300_VOC0712.pth
  • SSD300 v1 (original/old pool6 version) trained on VOC07
    • https://s3.amazonaws.com/amdegroot-models/ssd_300_voc07.tar.gz
• Our goal is to reproduce this table from the original paper

• Make sure you have jupyter notebook installed.
• Two alternatives for installing jupyter notebook:

  1. If you installed PyTorch with conda (recommended), then you should already have it. (Just navigate to the ssd.pytorch cloned repo and run): jupyter notebook

  2. If using pip:

# make sure pip is upgraded
pip3 install --upgrade pip
# install jupyter notebook
pip install jupyter
# Run this inside ssd.pytorch
jupyter notebook

• Now navigate to demo/demo.ipynb at http://localhost:8888 (by default) and have at it!

• Works on CPU (may have to tweak cv2.waitkey for optimal fps) or on an NVIDIA GPU
• This demo currently requires opencv2+ w/ python bindings and an onboard webcam
  • You can change the default webcam in demo/live.py
• Install the imutils package to leverage multi-threading on CPU:
  • pip install imutils
• Running python -m demo.live opens the webcam and begins detecting!

We have accumulated the following to-do list, which we hope to complete in the near future

• Still to come:
  • Train SSD300 with batch norm
  • Add support for SSD512 training and testing
  • Create a functional model definition for Sergey Zagoruyko's functional-zoo

• Wei Liu, et al. "SSD: Single Shot MultiBox Detector." ECCV2016.
• Original Implementation (CAFFE)
• A huge thank you to Alex Koltun and his team at Webyclip for their help in finishing the data augmentation portion.
• A list of other great SSD ports that were sources of inspiration (especially the Chainer repo):
  • Chainer, Keras, MXNet, Tensorflow