Comments (1)
Hi, @akshaylamba!
keras-rcnn is still a work-in-progress. I wouldn’t recommend experimenting with the package for a few weeks unless you’d like to contribute! If you’re interested in contributing, I recommend starting with the aforementioned malaria dataset and the ObjectDetection generator:
import keras_rcnn.datasets.malaria
training, test = keras_rcnn.datasets.malaria.load_data()
generator = keras_rcnn.preprocessing.ObjectDetectionGenerator()
generator = generator.flow(training)
validation_data = keras_rcnn.preprocessing.ObjectDetectionGenerator()
validation_data = generator.flow(test)
The ObjectDetection generator will provide the anchors needed for training, e.g.
image, (labels, bounding_boxes) = generator.next()
They look like this:
>>> image.shape
(1, 800, 600, 3)
>>> bounding_boxes.shape
(1, 50, 38, 18)
>>> labels.shape
(1, 50, 38, 72)
After you’ve generated a training sample, it’s more complicated. We’re in the process of refactoring our region proposal network (see the losses pull request). But in the next few days you should be able to pipe the output of the ObjectDetectionGenerator directly to the RegionProposalNetwork model.
#7 provides some details on a possible implementation.
If you have opinions about the design or architecture, please let us know! We’re actively looking for feedback. We aim to make this a package that’s made by and for the computer vision and Keras communities!
from keras-rcnn.
Related Issues (20)
- masks for dsb18 are off
- Generator limits image pixel range 0 to 1
- nans during RPN training HOT 4
- Remove boxes at the edge
- Model is not saved using model.save("model.h5") HOT 3
- KeyError when trying out the Example HOT 1
- AttributeError: module 'keras.engine' has no attribute 'topology' HOT 3
- Path Aggregation Network for Instance Segmentation
- Error while calculating val_loss using validation_data HOT 1
- Misspelling in README.rst
- README, the parameter position is wrong
- Error reading B&W object
- Unable to load the datasets in keras_rcnn
- Generating json of images with no object of concern.
- How to calculate the accuracy of the model that is generated?
- No module named 'keras_resnet' HOT 1
- AttributeError: module 'keras_resnet.models' has no attribute 'FPN2D50' HOT 1
- Tensorflow 2.x upgrade
- AttributeError: 'Node' object has no attribute 'output_masks' HOT 6
- share pretrained weights
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from keras-rcnn.