TensorFlow YOLO v3 Tutorial

If you hearing about "You Only Look Once" first time, you should know that it is an algorithm that uses convolutional neural networks for object detection. You only look once, or YOLO, is one of the fastest object detection algorithms out there. Though it is not the most accurate object detection algorithm, but it is a very good choice when we need real-time detection, without loss of too much accuracy.

To learn more about YOLO v3 and how it works please read my tutorial to understand how it works before moving to code:

YOLO v3 theory explained

Detailed code explanation you can find also on my website:

YOLO v3 code explained

Getting started

Prerequisites

This tutorial was written in Python 3.7 using Tensorflow (for deep learning), NumPy (for numerical computing), OpenCV (computer vision) and seaborn (visualization) packages. It's so wonderful that you can run object detection just using 4 simple libraries! First of all download all files from this tutorial. To install required libraries run:

pip install -r requirements.txt

Downloading official pretrained weights

Next we need to download official weights pretrained on COCO dataset. You can do this two ways. You can download it manually on same link below, create "weights" folder in repository and copy downloaded weights to that folder. Or you can simply do it with this command:

wget -P weights https://pjreddie.com/media/files/yolov3.weights

Convert weights into TensorFlow format

Now you need to run load_weights.py script, to convert downloaded weights to TensorFlow format.

python load_weights.py

Running the model

Now you are ready to run the model using detect_image.py or detect_video.pyscript. You can try to play around with iou_threshold and confidence_threshold parameters. My example images and video is in input folder. So you can put your examples there also or use different location.

Image usage example

If you'll open detect_image.py script at the last line you'll see:

main(0.5, 0.5, "input/office.jpg")

Here you can play with iou_threshold, confidence_threshold parameters and try you image for detection.

Here is few examples:

main(0.5, 0.5, "input/office.jpg")

main(0.5, 0.5, "input/cars.jpg")

main(0.5, 0.5, "input/zebra.jpg")

Video usage example

If you'll open detect_video.py script at the last line you'll see:

main(0.5, 0.5, "input/driving.mp4")

The detections will be saved as driving_yolo.mp4 file.Example video:

Future To-Do List

Write YOLOv3 in Keras
Train custom YOLOv3 detection model
Test YOLOv3 FPS performance on CS:GO

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