netron VS RMagick

Netron seems to be the best that I've seen so far. https://github.com/lutzroeder/netron

In exploring open-source projects, I've come across several promising tools capable of managing deep-learning models for images. Significantly, tools such as NETRON provide visualization of neural networks, while SHAP can be used for evaluating the significance of outputs.

We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.

How is this different from e.g Netron https://github.com/lutzroeder/netron

Netron (https://netron.app/) is the best and mostly used NN visualizer. Just save your model and then simply load it via netron to look its layers and weights. If you want a more complex visualization then you can also play with Zetane ( but its paid, also have a free version) engine.

require 'rmagick' include Magick module LambdaFunction class Handler def self.process(event:, context:) image_url = event['image_url'] my_image = ImageList.new(image_url) # TODO: Use rmagick to make your image transformations # Docs: https://rmagick.github.io { "success": true } end end end

mini_magick is much slower than YOLO. I hear that rmagick is well maintained these days, so you may want to use that.

To accomplish this project, I knew I would need some way to process the input images. Resizing the images was the easy bit. The more complex (and more important) task was to find the best way to relate a pixel's color in the source image to a dot's size in final animation. I felt that the relative luminance as described in this W3 accessibility document was a logical property to use in this case, and can be easily calculated with a color's RGB components. After determining what tasks I needed to fulfill, I determined that the RMagick library would be a good choice for this project.