RetinaNet-and-SSD-in-PyTorch-Detectron
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!
# First install Python server and clientpip 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 use Visdom in the browser:
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 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 weightscd 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.
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.
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
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:
https://s3.amazonaws.com/amdegroot-models/ssd_300_VOC0712.pth
https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
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
Make sure you have jupyter notebook installed.
Two alternatives for installing jupyter notebook:
# make sure pip is upgradedpip3 install --upgrade pip# install jupyter notebookpip install jupyter# Run this inside ssd.pytorchjupyter 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:
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.
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):
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