A higher performance PyTorch implementation of Single-Shot Refinement Neural Network for Object Detection. The official and original Caffe code can be found here.
Arch | Paper | Caffe Version | Our PyTorch Version |
---|---|---|---|
RefineDet320 | 80.0% | 79.52% | 79.81% |
RefineDet512 | 81.8% | 81.85% | 80.50% |
- Install PyTorch by selecting your environment on the website and running the appropriate command.
- Note: You should use at least PyTorch0.4.0
- Clone this repository.
- Note: We currently only support Python 3+.
- Then download the dataset by following the instructions below.
- 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
RefineDet.PyTorch/weights
dir: -
To train RefineDet320 or RefineDet512 using the train scripts
train_refinedet320.sh
andtrain_refinedet512.sh
. You can manually change them as you want.
./train_refinedet320.sh #./train_refinedet512.sh
- 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_refinedet.py
for options)
To evaluate a trained network:
./eval_refinedet.sh
You can specify the parameters listed in the eval_refinedet.py
file by flagging them or manually changing them.
- Original Implementation (CAFFE)
- A list of other great SSD ports that were sources of inspiration: