CoreMLHelpers | little easier to work with Core ML | iOS library

by PowerMobileWeb Swift Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | CoreMLHelpers Summary

CoreMLHelpers is a Swift library typically used in Mobile, iOS applications. CoreMLHelpers has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Types and functions that make it a little easier to work with Core ML in Swift.

Support

Quality

Security

License

Reuse

Support

CoreMLHelpers has a low active ecosystem.

It has 23 star(s) with 1 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

CoreMLHelpers has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CoreMLHelpers is current.

Quality

CoreMLHelpers has 0 bugs and 0 code smells.

Security

CoreMLHelpers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CoreMLHelpers code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CoreMLHelpers is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

CoreMLHelpers releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of CoreMLHelpers

Get all kandi verified functions for this library.

CoreMLHelpers Key Features

No Key Features are available at this moment for CoreMLHelpers.

CoreMLHelpers Examples and Code Snippets

No Code Snippets are available at this moment for CoreMLHelpers.

Community Discussions

Trending Discussions on CoreMLHelpers

Use first MLModel MLMultiArray output as second MLModel MLMultiArray Input

QUESTION

Use first MLModel MLMultiArray output as second MLModel MLMultiArray Input

Asked 2021-Jun-01 at 19:35

I have two CoreML MLMModels (converted from .pb).
The first model outputs a Float32 3 × 512 × 512 MLMultiArray, which basically describes an image.
The second model input is a Float32 1 × 360 × 640 × 3 MLMultiArray, which is also an image but with a different size.

I know that in theory, I can convert the second model input into an image, and then convert the first model output to an image (post-prediction), resize it, and feed the second model, but It feels not very efficient and there is already a significant delay caused by the models, so I'm trying to improve performance.

Is it possible to "resize"/"reshape"/"transposed" the first model output to match the second model input? I'm using https://github.com/hollance/CoreMLHelpers (by the amazing Matthijs Hollemans) helpers, but I don't really understand how to do it without damaging the data and keeping it as efficient as possible.

Thanks!

...

ANSWER

Answered 2021-Jun-01 at 19:35

You don't have to turn them into images. Some options for using MLMultiArrays instead of images:

You could take the 512x512 output from the first model and chop off a portion to make it 360x512, and then pad the other dimension to make it 360x640. But that's probably not what you want. In case it is, you'll have to write to code for this yourself.
You can also resize the 512x512 output to 360x640 by hand. To do this you will need to implement a suitable resizing option yourself (probably bilinear interpolation) or convert the data so you can use OpenCV or the vImage framework.
Let the model do the above. Add a ResizeBilinearLayer to the model, followed by a PermuteLayer or TransposeLayer to change the order of the dimensions. Now the image will be resized to 360x640 pixels, and the output of the first model is 1x360x640x3. This is easiest if you add these operation to the original model and then let coremltools convert them to the appropriate Core ML layers.

Source https://stackoverflow.com/questions/67787342

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CoreMLHelpers

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: