Comments (1)
@xiaoguaishoubaobao I think you will have to inject your own attention blocks at the stage you want. You need to look into model.py
file and introduce your attention blocks at the place you want. It would be nice to implement an attention integrated version of this. But I guess, it's not so generic especially the placement of attention module. It could be at FPN outputs or ResNet, etc. I once extracted FPN+ResNet part of the model and fed FPN output features into CBAM in my Siamese Network model. It is possible.
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Related Issues (20)
- Mask RCNN TF2 not working properly HOT 1
- ValueError: Error when checking input: expected input_image_meta to have shape (14,) but got array with shape (93,) HOT 1
- model = MaskRCNN(mode='training', model_dir=DEFAULT_LOGS_DIR, config=config) HOT 1
- AttributeError: module 'tensorflow' has no attribute 'random_shuffle'
- how to download this project HOT 1
- Create annotations for polyp image dataset HOT 1
- NotImplementedError: Save or restore weights that is not an instance of `tf.Variable` is not supported in h5, use `save_format='tf'` instead. Got a model or layer Conv2D with weights [<KerasVariable shape=(7, 7, 3, 64), dtype=float32, path=conv1/kernel>, <KerasVariable shape=(64,), dtype=float32, path=conv1/bias>] HOT 2
- Unresolved reference hdf5_format
- mrcnn not valid
- No module named 'keras.engine' HOT 5
- Problem in importing maskrcnn
- Maskrcnn import error HOT 3
- 'set_weights' error
- 0 accuracy when i run Mask RCNN with LabelMe software
- mask RCNN training takes too much time
- Training Mask R-CNN on custom data, but the training doesn't stop and produces no output or errors
- Updated version of this repo
- Hyperparamter Tuning Automated
- help please! StopIteration
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