Mask_RCNN_tf_2.x VS Mask_RCNN

Thus, if we want to use RTX 3000 series and later, we need to find a MRCNN that is Tensorflow 2.X compatible. Instead of updating the code myself, I looked through GitHub to see if anyone else had done this already. After some searching, I found a MRCNN package by BupyeongHealer that is compatible with Tensorflow 2.X versions. I implemented this package in Hent-AI by replacing the “mrcnn” folder (which has Matterport’s MRCNN) with the “mrcnn” folder from BupyeongHealer. Running Hent-AI at this point led to errors if trying to run Tensorflow 2.5 or newer due to the Layers class in Keras being moved from the Engine to the Layers module from Tensorflow 2.4 to 2.5, and Keras being moved from standalone to being part of the Tensorflow package itself. These errors were all eliminated by making the following modifications to “model.py” in the “mrcnn” folder:

The most widely used algorithms for classical feature detection today are "whatever opencv implements"

In terms of tech that's advancing at the moment? https://co-tracker.github.io/ if you want to track individual points, https://github.com/matterport/Mask_RCNN and its descendents if you want to detect, say, the cover of a book.

Mask R-CNN

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- Mask RCNN https://github.com/matterport/Mask_RCNN (open source, so also not Palantir's)

pip install git+https://github.com/matterport/Mask_RCNN

It is important to realize that to do its masking procedures, Hent-AI uses the Mask RCNN (MRCNN) package from Matterport. The problem with this version of MRCNN is that it is not compatible with Tensorflow 2.X versions, essentially limiting Hent-AI compatibility to strict Tensorflow 1.X versions. Since Tensorflow 1.15 is the last of the Tensorflow 1.X versions and uses CUDA 10.0, which supports a maximum compute capability of 7.5, this means that the last NVIDIA GPU series that is compatible with the original Hent-AI implementation is the RTX 2000 series. This is, of course, not optimal since it means that RTX 3000 series and later GPUs cannot be used despite their significant computing power and high VRAM.

I read that it could bethe problem with scipy version (https://github.com/matterport/Mask_RCNN/issues/2122) so I downgraded it, I also tried to modify shift = np.array([0, 0, 1., 1.]) in utils.py but nothing helped.

I am assuming you did a pip install of this github repository, or did you run pip install mrcnn. The mrcnn package on pypi is just an example package and doesn't have any useful functionality. In addition, where did you get the code from that you are trying to run, from someone else or did you write it yourself? Reason I am asking is because the import error is to be expected since there indeed is no InferenceConfig class defined in mrcnn.visualize.

Hello AI learners, check out my own development of Mask-RCNN supporting Tensorflow2.7.0 and Keras2.8.0. This is an edit of MRCNN which supports Tensoflow1.0, only.