There are 10, images in the VOC test set. We just processed them in seconds! That's 44 frames per second! If you were using Selective Search it would take you 6 hours to even extract region proposals for all of the images. We just ran a full detection pipeline in 4 minutes. Pretty cool. They are in the format specified for Pascal VOC submission. If you are interested in reproducing our numbers on the Pascal challenge you should use this weight file 1. It was trained with the IOU prediction we describe in the paper which gives slightly better mAP scores.
The numbers won't match exactly since I accidentally deleted the original weight file but they will be approximately the same. Running YOLO on test data isn't very interesting if you can't see the result. Instead of running it on a bunch of images let's run it on the input from a webcam! You will also need to pick a YOLO config file and have the appropriate weights file. Then run the command:. You will need a webcam connected to the computer that OpenCV can connect to or it won't work.
COCO is a large detection dataset from Microsoft with 80 object categories. If you are starting from scratch you can run these commands to detect objects in an image:. You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. Here's how to get it working on the Pascal VOC dataset.
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. Where x , y , width , and height are relative to the image's width and height. Let's just download it again because we are lazy. 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. In this example, let's train with everything except the validation set from so that we can test our model. Now we have all the images and the train set in one big list.
That's all we have to do for data setup! Now go to your Darknet directory. It works by dividing the image into regions and predicting bounding boxes and probabilities for each region. If you have any questions or just want to chat with me, feel free to leave a comment below or contact me on social media.
If you want to get continuous updates about my blog make sure to join my newsletter. Gilbert Tanner. What is Yolo? Installation To run keras-yolo3, you'll have to install the following packages: Tensorflow Keras Pillow The library was built for older Keras versions, so you might experience problems if you are using the newest versions. Tested versions: Keras 2. You can find the Dockerfile and docker-compose. Run YOLO detection.
We'll work through this step-by-step. Figure 2: Detect objects in image Running model on video If you don't specify the --image flag, you'll have to specify the path to a video as well as a path to save the output to.
Training custom model example To make the steps more transparent, we'll work through a real-world example. Generate your own annotation file and class names file. Using the custom model After the training has finished, you can use the model as I described in the "Testing installation by running a pre-trained model" section. Recommended Readings. Free Machine Learning Newsletter. It is equivalent to the command:. You don't need to know this if all you want to do is run detection on one image but it's useful to know if you want to do other things like run on a webcam which you will see later on.
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 So that's obviously not super useful but you can set it to different values to control what gets thresholded by the model. We have a very small model as well for constrained environments, yolov3-tiny.
To use this model, first download the weights:. Running YOLO on test data isn't very interesting if you can't see the result. Instead of running it on a bunch of images let's run it on the input from a webcam! Then run the command:. You will need a webcam connected to the computer that OpenCV can connect to or it won't work. You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. Here's how to get it working on the Pascal VOC dataset.
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. Where x , y , width , and height are relative to the image's width and height.
Let's just download it again because we are lazy. After a few minutes, this script will generate all of the requisite files.
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