Keras implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár.

1. Clone this repository.
2. In the repository, execute pip install . --user. Note that due to inconsistencies with how tensorflow should be installed, this package does not define a dependency on tensorflow as it will try to install that (which at least on Arch Linux results in an incorrect installation). Please make sure tensorflow is installed as per your systems requirements. Also, make sure Keras 2.1.3 is installed.
3. As of writing, this repository requires the master branch of keras-resnet (run pip install --user --upgrade git+https://github.com/broadinstitute/keras-resnet).
4. Optionally, install pycocotools if you want to train / test on the MS COCO dataset by running pip install --user git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI.

keras-retinanet can be trained using this script. Note that the train script uses relative imports since it is inside the keras_retinanet package. If you want to adjust the script for your own use outside of this repository, you will need to switch it to use absolute imports.

If you installed keras-retinanet correctly, the train script will be installed as retinanet-train. However, if you make local modifications to the keras-retinanet repository, you should run the script directly from the repository. That will ensure that your local changes will be used by the train script.

The default backbone is 'resnet50'. You can change this using the '--backbone=xxx' argument in the running script. xxx can be one of the backbones in resnet models (resnet50, resnet101, resnet152) or mobilenet models (mobilenet128_1.0, mobilenet128_0.75, mobilenet160_1.0, etc). The different options are defined by each model in their corresponding python scripts (resnet.py, mobilenet.py, etc).

For training on Pascal VOC, run:

# Running directly from the repository:
keras_retinanet/bin/train.py pascal /path/to/VOCdevkit/VOC2007

# Using the installed script:
retinanet-train pascal /path/to/VOCdevkit/VOC2007

For training on MS COCO, run:

# Running directly from the repository:
keras_retinanet/bin/train.py coco /path/to/MS/COCO

# Using the installed script:
retinanet-train coco /path/to/MS/COCO

The pretrained MS COCO model can be downloaded here. Results using the cocoapi are shown below (note: according to the paper, this configuration should achieve a mAP of 0.343).

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.325
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.513
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.342
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.354
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.465
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.437
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.464
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.510
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623

For training on OID, run:

# Running directly from the repository:
keras_retinanet/bin/train.py oid /path/to/OID

# Using the installed script:
retinanet-train oid /path/to/OID

# You can also specify a list of labels if you want to train on a subset
# by adding the argument 'labels_filter':
keras_retinanet/bin/train.py oid /path/to/OID --labels_filter=Helmet,Tree

For training on a custom dataset, a CSV file can be used as a way to pass the data. See below for more details on the format of these CSV files. To train using your CSV, run:

# Running directly from the repository:
keras_retinanet/bin/train.py csv /path/to/csv/file/containing/annotations /path/to/csv/file/containing/classes

# Using the installed script:
retinanet-train csv /path/to/csv/file/containing/annotations /path/to/csv/file/containing/classes

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

1. Create a model by calling for instance keras_retinanet.models.resnet50_retinanet and compile it. Empirically, the following compile arguments have been found to work well:

model.compile(
    loss={
        'regression'    : keras_retinanet.losses.smooth_l1(),
        'classification': keras_retinanet.losses.focal()
    },
    optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001)
)

2. Create generators for training and testing data (an example is show in keras_retinanet.preprocessing.PascalVocGenerator).
3. Use model.fit_generator to start training.

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, cls1, cls2, ...)).

Loading models can be done in the following manner:

from keras_retinanet.models.resnet import custom_objects
model = keras.models.load_model('/path/to/model.h5', custom_objects=custom_objects)

Execution time on NVIDIA Pascal Titan X is roughly 75msec for an image of shape 1000x600x3.

The CSVGenerator provides an easy way to define your own datasets. It uses two CSV files: one file containing annotations and one file containing a class name to ID mapping.

The CSV file with annotations should contain one annotation per line. Images with multiple bounding boxes should use one row per bounding box. Note that indexing for pixel values starts at 0. The expected format of each line is:

path/to/image.jpg,x1,y1,x2,y2,class_name

Some images may not contain any labeled objects. To add these images to the dataset as negative examples, add an annotation where x1, y1, x2, y2 and class_name are all empty:

path/to/image.jpg,,,,,

A full example:

/data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_002.jpg,215,312,279,391,cat
/data/imgs/img_002.jpg,22,5,89,84,bird
/data/imgs/img_003.jpg,,,,,

This defines a dataset with 3 images. img_001.jpg contains a cow. img_002.jpg contains a cat and a bird. img_003.jpg contains no interesting objects/animals.

The class name to ID mapping file should contain one mapping per line. Each line should use the following format:

class_name,id

Indexing for classes starts at 0. Do not include a background class as it is implicit.

For example:

cow,0
cat,1
bird,2

Creating your own dataset does not always work out of the box. There is a debug.py tool to help find the most common mistakes.

Particularly helpful is the --annotations flag which displays your annotations on the images from your dataset. Annotations are colored in green when there are anchors available and colored in red when there are no anchors available. If an annotation doesn't have anchors available, it means it won't contribute to training. It is normal for a small amount of annotations to show up in red, but if most or all annotations are red there is cause for concern. The most common issues are that the annotations are too small or too oddly shaped (stretched out).

Example output images using keras-retinanet are shown below.

• This repository requires Keras 2.1.3.
• This repository is tested using OpenCV 3.4.
• Warnings such as UserWarning: Output "non_maximum_suppression_1" missing from loss dictionary. can safely be ignored. These warnings indicate no loss is connected to these outputs, but they are intended to be outputs of the network for the user (ie. resulting network detections) and not loss outputs.

Contributions to this project are welcome.

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