For Robocup
python: python3 pytorch: tested on 0.3.1
Put anywhere
mkdir weights
cd weights
wget -O vgg16.pth https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
The dataset seems to be very 'fragile', so we need to use a small learning rate
python3 train.py --lr 0.00001 --data_root [/path/to/handles_dataset]
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).
- Note: For training, we currently only support VOC, but are adding COCO and hopefully ImageNet soon.
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.
# 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.
- 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:
- 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 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
- You can change the default webcam in
- 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 you can expect to be done in the very 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):