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When you see that average loss 0. The final average loss can be from 0. For example, you stopped training after iterations, but the best result can give one of previous weights , , It can happen due to over-fitting. You should get weights from Early Stopping Point :. At first, in your file obj. If you use another GitHub repository, then use darknet. Choose weights-file with the highest mAP mean average precision or IoU intersect over union.

So you will see mAP-chart red-line in the Loss-chart Window. Example of custom object detection: darknet. In the most training issues - there are wrong labels in your dataset got labels by using some conversion script, marked with a third-party tool, If no - your training dataset is wrong. What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object with a little gap?

Mark as you like - how would you like it to be detected. General rule - your training dataset should include such a set of relative sizes of objects that you want to detect:. So the more different objects you want to detect, the more complex network model should be used.

Only if you are an expert in neural detection networks - recalculate anchors for your dataset for width and height from cfg-file: darknet. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors. Increase network-resolution by set in your. With example of: train. Skip to content. Star View license. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

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? Launching Xcode If nothing happens, download Xcode and try again. Launching Visual Studio Code Your codespace will open once ready. This branch is 6 commits ahead , 14 commits behind AlexeyAB:master.

This branch is 6 commits ahead of AlexeyAB:master. Open pull request. Latest commit. Git stats 2, commits. Failed to load latest commit information. View code. Releases 6 tags. Packages 0 No packages published. 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.