Darknet prints out the objects it detected, its confidence, and how long it took to find them. Instead, it saves them in predictions. You can open it to see the detected objects. Since we are using Darknet on the CPU it takes around seconds per image.
If we use the GPU version it would be much faster. The detect command is shorthand for a more general version of the command. It is equivalent to the command:. Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row. Instead you will see a prompt when the config and weights are done loading:. Once it is done it will prompt you for more paths to try different images. Use Ctrl-C to exit the program once you are done.
By default, YOLO only displays objects detected with a confidence of. For example, to display all detection you can set the threshold to We have a very small model as well for constrained environments, yolov3-tiny. To use this model, first download the weights:. Then run the command:. You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets.
You can find links to the data here. To get all the data, make a directory to store it all and from that directory run:. Now we need to generate the label files that Darknet uses. Darknet wants a. After a few minutes, this script will generate all of the requisite files. In your directory you should see:. Darknet needs one text file with all of the images you want to train on. Now we have all the trainval and the trainval set in one big list. Now go to your Darknet directory.
For training we use convolutional weights that are pre-trained on Imagenet. We use weights from the darknet53 model. This is the first one. In this blog, we will see how to setup YOLO with darknet and run it. We will also demonstrate the various choices you have with YOLO in terms of accuracy, speed and cost, enabling you to make a more informed choice of how you would want to run your models. The content in the blog is not unique.
However if you are starting with YOLO, this is the first thing you need to do. It should go all fine, and you have the darknet platform installed. The next step will be to download pre-trained weights. We will download the default weights and also the optimised weights and try them.
Note that the config files for these weights are already downloaded and the in the cfg directory. We can quickly run the object detector with the default weights. We can run inference on the same picture with yolo-tiny a smaller, faster but slightly less accurate model. 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.