/ssd.pytorch

A PyTorch Implementation of Single Shot MultiBox Detector

Primary LanguagePythonMIT LicenseMIT

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

  • 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 only support VOC, but are adding COCO and hopefully ImageNet soon.
  • UPDATE: We have switched from PIL Image support to cv2. The plan is to create a branch that uses PIL as well.

Datasets

To make things easy, we provide a simple VOC dataset loader that inherits 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>

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.
    • 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)

Evaluation

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.

Performance

VOC2007 Test

mAP
Original Converted weiliu89 weights From scratch w/o data aug From scratch w/ data aug
77.2 % 77.26 % 58.12% 77.43 %
Evaluation report for the current version

VOC07 metric? Yes

AP for aeroplane = 0.8172
AP for bicycle = 0.8544
AP for bird = 0.7571
AP for boat = 0.6958
AP for bottle = 0.4990
AP for bus = 0.8488
AP for car = 0.8577
AP for cat = 0.8737
AP for chair = 0.6147
AP for cow = 0.8233
AP for diningtable = 0.7917
AP for dog = 0.8559
AP for horse = 0.8709
AP for motorbike = 0.8474
AP for person = 0.7889
AP for pottedplant = 0.4996
AP for sheep = 0.7742
AP for sofa = 0.7913
AP for train = 0.8616
AP for tvmonitor = 0.7631
Mean AP = 0.7743

FPS

GTX 1060: ~45.45 FPS

Demos

Use a pre-trained SSD network for detection

Download a pre-trained network

SSD results on multiple datasets

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

Try the webcam demo

  • 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!

TODO

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

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

References