More details in the original Faster R-CNN implementation. If you use Docker, the code has been verified to work on this Docker container. Installation Install dependencies pip3 install -r requirements.txt Clone this repository Run setup from the repository root directory python3 setup.py install...
Indeed, in our previous implementation we had exactly what you wrote, but loading pre-trained models from torchvision (which had running_mean, weight, etc) was not possible out of the box and we had to perform some net surgery. In the end, it was just simpler to have this small ...
It currently hard-code ROIAlign in the implementation, but that can be made more generic later on. Also, the requirement of passing the scales is not strictly necessary, as they can be inferred from the size of the feature map / size of original image, which is available thanks to the B...
This implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do let us know. 本文大部分内容遵循Mask...
Differences from the Official Paper This implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do ...
There isn’t a universally accepted format to store segmentation masks. Some datasets save them as PNG images, others store them as polygon points, and so on. To handle all these cases, our implementation provides a Dataset class that you inherit from and then override a few functions to rea...
Differences from the Official Paper This implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do ...
Mask R-CNN is a fairly large model. Especially that our implementation uses ResNet101 and FPN. So you need a modern GPU with 12GB of memory. It might work on less, but I haven’t tried. I usedAmazon’s P2 instancesto train this model, and given the small dataset, trainin...
# Faster R-CNN in MXNet with distributed implementation and data parallelization  ## Why? There exist good implementations of Faster R-CNN yet they lack support for recen...
This is a Pytorch implementation of Mask R-CNN that is in large parts based on Matterport's Mask_RCNN. Matterport's repository is an implementation on Keras and TensorFlow. The following parts of the README are excerpts from the Matterport README. Details on the requirements, training on MS...