RetinaNet

An implementation of RetinaNet in PyTorch.

Installation
Training
Evaluation
Todo
Credits

Installation

Install PyTorch and torchvision.
For faster data augmentation, install pillow-simd:

pip uninstall -y pillow
pip install pillow-simd

Training

COCO 2017

First, install pycocotools:

git clone https://github.com/pdollar/coco/
cd coco/PythonAPI
make
python setup.py install
cd ../..
rm -r coco

Then download COCO 2017 into ./datasets/COCO/:

cd datasets
mkdir COCO
cd COCO

If your using wget:

wget http://images.cocodataset.org/zips/train2017.zip &&
wget http://images.cocodataset.org/zips/val2017.zip &&
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip

If your using aria2c (recommended on for higher bandwidth connections and for allowing resumption of the download. Tune the number of max concurrent downloads (-j) and max connections per server (-x) as needed:

aria2c -x 10 -j 10 http://images.cocodataset.org/zips/train2017.zip &&
aria2c -x 10 -j 10 http://images.cocodataset.org/zips/val2017.zip &&
aria2c -x 10 -j 10 http://images.cocodataset.org/annotations/annotations_trainval2017.zip

unzip *.zip
rm *.zip

Then just run:

python train_coco.py

Pascal VOC

cd datasets
mkdir VOC
cd VOC

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar &&
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar &&
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar &&
aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar &&
aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

tar xf *.tar
rm *.tar

Then just run:

python train_voc.py

Custom Dataset

Lots to write here. 😉

Evaluation

To evaluate an image on a trained model:

python eval.py [checkpoint_path] [image_path]

This will create an image (output.jpg) with bounding box annotations.

Todo

Finish converting the COCO dataset class to work with batches.
Train COCO 2017 for 90,000 iterations and save a reusable checkpoint.
Try training on Pascal VOC and add download instructions.
Produce bounding box outputs for a few sanity check images.
Upload trained weights to Github releases.
Train on the 🔮magic proprietary dataset ✨.

c0nn3r/RetinaNet