GitHub - toandaominh1997/EfficientDet.Pytorch: Implementation EfficientDet: Scal...
source link: https://github.com/toandaominh1997/EfficientDet.Pytorch
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README.md
EfficientDet: Scalable and Efficient Object Detection, in PyTorch
A PyTorch implementation of EfficientDet from the 2019 paper by Mingxing Tan Ruoming Pang Quoc V. Le Google Research, Brain Team. The official and original: comming soon.
Table of Contents
Installation
- 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.
Datasets
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.
COCO
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
VOC Dataset
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>
Training EfficientDet
- To train EfficientDet 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)
Evaluation
To evaluate a trained network:
Comming soon
Performance
Demo
TODO
We have accumulated the following to-do list, which we hope to complete in the near future
- Still to come:
- EfficientDet
- GPU-Parallel
- Weighted Feature Fusion
- Pretrained model
- Demo
- Model zoo
Authors
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.
References
- tanmingxing, rpang, qvl, et al. "EfficientDet: Scalable and Efficient Object Detection." EfficientDet.
- A list of other great EfficientDet ports that were sources of inspiration (especially the Chainer repo):
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