Yolo v3 is an algorithm that uses deep convolutional neural networks to detect objects.

Kaggle notebook

This project is written in Python 3.6.6 using Tensorflow (deep learning), NumPy (numerical computing), Pillow (image processing), OpenCV (computer vision) and seaborn (visualization) packages.

pip install -r requirements.txt

Let's download official weights pretrained on COCO dataset.

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

Save the weights using load_weights.py script.

python load_weights.py

Now you can run the model using detect.py script. Don't forget to set the IoU (Interception over Union) and confidence thresholds.

python detect.py <images/video> <iou threshold> <confidence threshold> <filenames>

Let's run an example using sample images.

python detect.py images 0.5 0.5 data/images/dog.jpg data/images/office.jpg

Then you can find the detections in the detections folder.
You should see something like this.

detection_1.jpg

detection_2.jpg

You can also run the script with video files.

python detect.py video 0.5 0.5 data/video/shinjuku.mp4

The detections will be saved as detections.mp4 file.

• Model training

• Yolo v3 official paper
• A Tensorflow Slim implementation
• ResNet official implementation
• DeviceHive video analysis repo
• A Street Walk in Shinjuku, Tokyo, Japan