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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.
pip install numpy --user
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.python setup.py build_ext --inplace to compile Cython code first.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.An example of testing the network can be seen in this Notebook. In general, inference of the network works as follows:
boxes, scores, labels = model.predict_on_batch(inputs)
Where boxes are shaped (None, None, 4) (for (x1, y1, x2, y2)), scores is shaped (None, None) (classification score) and labels is shaped (None, None) (label corresponding to the score). In all three outputs, the first dimension represents the shape and the second dimension indexes the list of detections.
Loading models can be done in the following manner:
from keras_retinanet.models import load_model
model = load_model('/path/to/model.h5', backbone_name='resnet50')
Execution time on NVIDIA Pascal Titan X is roughly 75msec for an image of shape 1000x800x3.
The training procedure of keras-retinanet works with training models. These are stripped down versions compared to the inference model and only contains the layers necessary for training (regression and classification values). If you wish to do inference on a model (perform object detection on an image), you need to convert the trained model to an inference model. This is done as follows:
# Running directly from the repository:
keras_retinanet/bin/convert_model.py /path/to/training/model.h5 /path/to/save/inference/model.h5
# Using the installed script:
retinanet-convert-model /path/to/training/model.h5 /path/to/save/inference/model.h5
Most scripts (like retinanet-evaluate) also support converting on the fly, using the --convert-model argument.
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), mobilenet models (mobilenet128_1.0, mobilenet128_0.75, mobilenet160_1.0, etc), densenet models or vgg models. The different options are defined by each model in their corresponding python scripts (resnet.py, mobilenet.py, etc).
Trained models can't be used directly for inference. To convert a trained model to an inference model, check here.
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 0.357).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.350
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.537
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.374
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.306
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.491
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.533
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.577
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.681
For training on Open Images Dataset OID or taking place to the OID challenges, 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
# You can also specify a parent label if you want to train on a branch
# from the semantic hierarchical tree (i.e a parent and all children)
(https://storage.googleapis.com/openimages/challenge_2018/bbox_labels_500_hierarchy_visualizer/circle.html)
# by adding the argument 'parent-label':
keras_retinanet/bin/train.py oid /path/to/OID --parent-label=Boat
For training on KITTI, run:
# Running directly from the repository:
keras_retinanet/bin/train.py kitti /path/to/KITTI
# Using the installed script:
retinanet-train kitti /path/to/KITTI
If you want to prepare the dataset you can use the following script:
https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/prepare_kitti_data.py
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:
keras_retinanet.models.backbone('resnet50').retinanet(num_classes=80) 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)
)
keras_retinanet.preprocessing.pascal_voc.PascalVocGenerator).model.fit_generator to start training.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.
If you have a project based on keras-retinanet and would like to have it published here, shoot me a message on Slack.
Contributions to this project are welcome.
Feel free to join the #keras-retinanet Keras Slack channel for discussions and questions.
UserWarning: No training configuration found in save file: the model was not compiled. Compile it manually., should I be worried? This warning can safely be ignored during inference.ValueError: not enough values to unpack (expected 3, got 2) during inference, what to do?. This is because you are using a train model to do inference. See https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model for more information.--weights argument when training. Keras will load models, even if the number of classes don't match (it will simply skip loading of weights when there is a mismatch). Run for example retinanet-train --weights snapshots/some_coco_model.h5 pascal /path/to/pascal to transfer weights from a COCO model to a PascalVOC training session. If your dataset is small, you can also use the --freeze-backbone argument to freeze the backbone layers.0, what is going on? This mostly happens when none of the anchors "fit" on your objects, because they are most likely too small or elongated. You can verify this using the debug tool.model.load_weights(weights_path, by_name=True) and saving this model. If the change has been too significant, you should retrain your model (you can try to load in the weights from your old model when starting training, this might be a better starting position than ImageNet).ModuleNotFoundError: No module named 'keras_retinanet.utils.compute_overlap', how do I fix this? Most likely you are running the code from the cloned repository. This is fine, but you need to compile some extensions for this to work (python setup.py build_ext --inplace).retinanet-debug.此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
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