Yolo v3 Object Detection in Tensorflow
Yolo v3 is an algorithm that uses deep convolutional neural networks to detect objects.
Kaggle notebook
Getting started
Prerequisites
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
Downloading official pretrained weights
Let's download official weights pretrained on COCO dataset.
wget -P weights https://pjreddie.com/media/files/yolov3.weights
Save the weights in Tensorflow format
Save the weights using load_weights.py script.
python load_weights.py
Running the model
Now you can run the model using detect.py script. Don't forget to set the IoU (Intersection over Union) and confidence thresholds.
Usage
python detect.py <images/video> <iou threshold> <confidence threshold> <filenames>
Images example
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
Video example
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.
To-Do List
- Model training