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Keras RetinaNet

Keras implementation of RetinaNet object detection as described in this paper by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár.

Training

An example on how to train keras-retinanet can be found here.

Usage

For training on Pascal VOC, run:

python examples/train_pascal.py <path to VOCdevkit/VOC2007>

For training on MS COCO, run:

python examples/train_coco.py <path to MS COCO>

In general, the steps to train on your own datasets are:

  1. Create a model by calling keras_retinanet.models.ResNet50RetinaNet and compile it. Empirically, the following compile arguments have been found to work well:
model.compile(
    loss={
        'regression'    : keras_retinanet.losses.regression_loss,
        'classification': keras_retinanet.losses.focal_loss()
    },
    optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001)
)
  1. Create generators for training and testingdata (an example is show in keras_retinanet.preprocessing.PascalVocIterator). These generators should generate an image batch (shaped (batch_id, height, width, channels)) and a target batch (shaped (batch_id, num_anchors, 5)). Currently, a limitation is that batch_size must be equal to 1.
  2. Use model.fit_generator to start training.

Testing

An example of testing the network can be seen in this Notebook. In general, output can be retrieved from the network as follows:

_, _, detections = model.predict_on_batch(inputs)

Where detections are the resulting detections, shaped (None, None, 4 + num_classes) (for (x1, y1, x2, y2, bg, cls1, cls2, ...)).

Execution time on NVidia Pascal Titan X is roughly 55msec for an image of shape 1000x600x3.

Status

Example result of RetinaNet on MS COCO Example result of RetinaNet on MS COCO Example result of RetinaNet on MS COCO

Todo's

  • Allow batch_size > 1.
  • Compare result w.r.t. paper results.
  • Add unit tests
  • Configure CI

Notes

  • This implementation currently uses the softmax activation to classify boxes. The paper mentions a sigmoid activation instead. Given the origin of parts of this code, the softmax activation method was easier to implement. A comparison between sigmoid and softmax would be interesting, but left as unexplored.
  • This repository depends on an unmerged PR of keras-resnet. For now, it can be installed by manually installing this branch.
  • This repository is tested on Keras version 2.0.8, but should also work on 2.0.7.
  • This repository is tested using OpenCV 3.3 (3.0+ should be supported).

Any and all contributions to this project are welcome.

Discussions

Feel free to join the #keras-retinanet Keras Slack channel for discussions and questions.