Kingfisher VS Mask_RCNN

The 3rd party library I use GRDB SwiftyUserDefaults Kingfisher SwiftDate Popovers

Saving URLs definitely the way to go. Would recommend using a library such as Kingfisher to do the image loading/ caching for you.

I also looked into this github library but wanted to first ask if anyone's had any experience using it.

You can checkout Kingfisher library (it downloads image, and caches it). Usage is fairly simple but I would recomment that you create extension for the UIIMageView, somthing in line like this:

In native iOS, most people use Kingfisher, and i'd love to have something as good as that on our RN app. Perhaps just some pure JS library that you could plug on the existing image component, and that would handle the saving-to-disk and fetching of the image?

Kingfisher - Download and cache images

We are using https://github.com/onevcat/Kingfisher for smooth image loading in collection view. If the image data are not in regular text file, how can we integrate CoreData's blob data with Kingfisher?

Make sure the proper target is selected in the Add to target in the dialog as shown above, then click Add Package button. The package should successfully be added to your project. Now following the same process, this time search for the Kingfisher package using the GitHub URL as the following https://github.com/onevcat/Kingfisher.

Kingfisher - A lightweight, pure-Swift library for downloading and caching images from the web. Language: Swift.

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.