README.md

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.

Table of Contents

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

       

Installation

• Install PyTorch by selecting your environment on the website and running the appropriate command.
• Clone this repository.
  • Note: We only guarantee full functionality with 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 only support VOC, but are adding COCO and hopefully ImageNet soon.

Datasets

To make things easy, we provide a simple VOC dataset loader that enherits torch.utils.data.Dataset making it fully compatible with the torchvision.datasets API.

VOC Dataset

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>

Ensure the following directory structure (as specified in VOCdevkit):

VOCdevkit/                                  % development kit
VOCdevkit/VOC2007/ImageSets                 % image sets
VOCdevkit/VOC2007/Annotations               % annotation files
VOCdevkit/VOC2007/JPEGImages                % images
VOCdevkit/VOC2007/SegmentationObject        % segmentations by object
VOCdevkit/VOC2007/SegmentationClass         % segmentations by class

Training SSD

• 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

• Training Parameter Options:

'--version', default='v2', help='conv11_2(v2) or pool6(v1) as last layer'
'--basenet', default='vgg16_reducedfc.pth', help='pretrained base model'
'--jaccard_threshold', default=0.5, type=float, help='Min Jaccard index for matching'
'--batch_size', default=16, type=int, help='Batch size for training'
'--num_workers', default=4, type=int, help='Number of workers used in dataloading'
'--iterations', default=120000, type=int, help='Number of training epochs'
'--cuda', default=True, type=bool, help='Use cuda to train model'
'--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate'
'--momentum', default=0.9, type=float, help='momentum'
'--weight_decay', default=5e-4, type=float, help='Weight decay for SGD'
'--gamma', default=0.1, type=float, help='Gamma update for SGD'
'--log_iters', default=True, type=bool, help='Print the loss at each iteration'
'--visdom', default=True, type=bool, help='Use visdom to for loss visualization'
'--save_folder', default='weights/', help='Location to save checkpoint models'

• 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.

Evaluation

To evaluate a trained network:

python test.py

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

Demos

Use a pre-trained SSD network for detection

Download a pre-trained network

• 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 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

Try the demo notebook

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

TODO

We have accumulated the following to-do list, which you can expect to be done in the very near future

• Complete data augmentation (in progress)
• Train SSD300 with batch norm (in progress)
• Webcam demo (in progress)
• Add support for SSD512 training and testing
• Add support for COCO dataset
• Create a functional model definition for Sergey Zagoruyko's functional-zoo (in progress)

References

• Wei Liu, et al. "SSD: Single Shot MultiBox Detector." ECCV2016.
• Original Implementation (CAFFE)
• A list of other great SSD ports that were sources of inspiration (especially the Chainer repo):
  • Chainer, Keras, MXNet, Tensorflow