A PyTorch implementation of EfficientDet from the 2019 paper by Mingxing Tan Ruoming Pang Quoc V. Le Google Research, Brain Team.
- Install PyTorch by selecting your environment on the website and running the appropriate command.
- Clone this repository.
- Note: We currently only support Python 3.6+.
- 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
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>
- To train EfficientDet using the train script simply specify the parameters listed in
train.pyas 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:
We have accumulated the following to-do list, which we hope to complete in the near future
- Still to come:
- [x] EfficientDet
- [x] GPU-Parallel
- [ ] Weighted Feature Fusion
- [ ] Pretrained model
- [ ] Demo
- [ ] Model zoo
Toan Dao Minh
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