An implementation of RetinaNet in PyTorch.

Related tags

Deep Learning pytorch object-detection retinanet
Overview

RetinaNet

An implementation of RetinaNet in PyTorch.

RetinaNet Structure

Installation

  1. Install PyTorch and torchvision.
  2. For faster data augmentation, install pillow-simd:
pip uninstall -y pillow
pip install pillow-simd

Training

COCO 2017

  1. First, install pycocotools:
git clone https://github.com/pdollar/coco/
cd coco/PythonAPI
make
python setup.py install
cd ../..
rm -r coco
  1. 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

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://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

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

Credits

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Comments
  • I think focal_loss.py is wromg

    I think focal_loss.py is wromg

    Hi. I refer your code for my focal loss project, but I think there is mistake in focal_loss.py .

    cross_entropy = F.cross_entropy(output, target)
cross_entropy_log = torch.log(cross_entropy)

    should be

    logpt = -F.cross_entropy(input, target)
pt = torch.exp(logpt)

    Please confirm it. Best regards.

    opened by doiken23 3
  • classification branch

    classification branch

    @c0nn3r HI

    For focal loss. The classification branch uses the sigmoid function.

    Why background class is considered in the classification branch??such as coco, num_classes=80 instead of 81.

    opened by foralliance 0
  • about cal the regression loss

    about cal the regression loss

    hello sir, thx very much for the clear and clean repository. there is a small question about cal the regression loss, what's the meaning of those sentences when cal the regression loss in the end of file losses.py?

                targets = targets/torch.Tensor([[0.1, 0.1, 0.2, 0.2]]).cuda()

    and

                regression_loss = torch.where(
                torch.le(regression_diff, 1.0 / 9.0),
                0.5 * 9.0 * torch.pow(regression_diff, 2),
                regression_diff - 0.5 / 9.0
            )

    why not cal the L2 loss directily? is there any trick?

    thx again for your attention and reply

    opened by CharmsYANG 0
  • About the training log?

    About the training log?

    Would you please post the whole training log of this retina net, which including box loss and class loss? When I trained it from scratch, I found it took too long to converge to a relatively good results. Thanks a lot!!!

    opened by YJHMITWEB 0
Owner
Conner Vercellino
Interested in NLP, metalearning and other random machine learning topics.
Conner Vercellino
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