RMagick VS models

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.

Uh, maybe they didn't feel the need to look. I already pointed you to the ONNX project. Here are some ONNX-based. These are just the ones being shared with the community. The limit of AMD's responsibility is writing the low-level libraries to support ONNX.

I am new to object detection models and I need help running my object detection Darknet YOLOv4-Tiny Model In Unity Barracuda. I trained my model and then i converted it to ONNX format with 2 methods. One method was using pytorch-YOLOv4 from github and the other by converting my model to tensorflow and then to onnx and shown here: "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb"

Then i tried to convert it again using another method that i found here "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb" in order to convert it from darknet to tensorflow and then to onnx but i didn't have any luck.

Here we use the GPT-2 model distributed by the ONNX official. Download GPT-2-LM-HEAD from the link.

Download pre-trained models from the ONNX Model Zoo

Instructions to reproduce the problem: I am trying to convert a proprietary model at work but for now i'll use mobilenetv2-7.onnx to explain/reproduce the issue.

One approach could be run a pre-trained object detector (like one of these) on each frame and then a simple object tracker on top of it (like this).