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.
- 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).
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
To train SSD using the train script simply specify the parameters listed in
train.py as a flag or manually change them.
- 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.py file by flagging them or manually changing them.
|Original||Converted weiliu89 weights||From scratch w/o data aug||From scratch w/ data aug|
|77.2 %||77.26 %||58.12%||77.43 %|
GTX 1060: ~45.45 FPS
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 trained on VOC0712 (newest PyTorch weights)
- SSD300 trained on VOC0712 (original Caffe weights)
- Our goal is to reproduce this table from the original paper
![SSD results on multiple datasets](https://camo.githubusercontent.com/9b7e16ebc07715ef620d746af285a1a4dc90842b04bc3ae024b94c3a7f7e95ea/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373645f726573756c74732e706e67 =800pxx)
Try the demo notebook
Make sure you have jupyter notebook installed.
Two alternatives for installing jupyter notebook:
- If you installed PyTorch with conda (recommended), then you should already have it. (Just navigate to the ssd.pytorch cloned repo and run):
- If using pip:
make sure pip is upgraded
pip3 install --upgrade pip
install jupyter notebook
pip install jupyter
Run this inside ssd.pytorch
Now navigate to
demo/demo.ipynb at http://localhost:8888 (by default) and have at it!
Try the webcam demo
- Works on CPU (may have to tweak
cv2.waitkeyfor 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
- Install the imutils package to leverage multi-threading on CPU:
pip install imutils
python -m demo.liveopens 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:
- Support for the MS COCO dataset
- Support for SSD512 training and testing
- Support for training on custom datasets
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.
- 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