Yolo darknet android mega

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The outputs look like these. Comparing the results of yolov3 and yolo-tiny, we can see that yolo-tiny is much faster but less accurate. Depending on your application you can choose a models that are faster or are more accurate. However we have make a few changes to leverage the power of a GPU. Its the first line in the Makefile and run make again. Comparing the speeds, we can see that GPU delivers the same results in much shorter time.

So it you can afford expensive hardware like GPUs, you can be much faster and more accurate. You can also choose to use Yolov3 model with a different size to make it faster. We use the same weights file, but change 2 lines in the config. Change the width and height in the config file from to , and rerun the detect command. Skip to content. Setup Yolo with Darknet The content in the blog is not unique.

The outputs look like these Comparing the results of yolov3 and yolo-tiny, we can see that yolo-tiny is much faster but less accurate. Permalink master. Branches Tags. Could not load branches. Could not load tags. A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior.

Are you sure you want to create this branch? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Introduction II. Development Environment III. Project Construction 3. Raw Blame. Edit this file. Open with Desktop View raw View blame.

Introduction This project will develop and compile the source code of Yolo-v3 in android studio and deploy it to android mobile phone to realize the call of Yolo-v3 network by using mobile phone hardware platform. Development Environment OS: Ubuntu Project operation Run the project on Xiaomi 8, you can see the file information read in the logcat of android studio 3, and the configuration of Yolo-v3-tiny network. Logcat will print out the predicted object name, probability and processing time.

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