Embedded Files
Data Description
Data Description
Udacity Annotated Driving Dataset 1
The dataset includes driving in Mountain View California and neighboring cities during daylight conditions. It contains over 65,000 labels across 9,423 frames collected from a Point Grey research cameras running at full resolution of 1920x1200 at 2hz. The dataset was annotated by CrowdAI using a combination of machine learning and humans.
Labels
- Car
- Truck
- Pedestrian
CSV Format
- xmin
- ymin
- xmax
- ymax
- frame
- label
- preview url for frame
Data Source Link:
https://github.com/udacity/self-driving-car/tree/master/annotations
Implementation
Implementation
Detection Settings
- Python 3
- Numpy
- OpenCV Python
Method
We use OpenCV DNN (Deep Neural Network) module as running inference on images with YOLO models and configuration files. We can see the result as follow:
Training Settings
- Darknet
- MSI GTX 1070 (1 GPU)
- 720 images of Udacity Annotated Driving Dataset 1
- Yolov3-tiny weight and configuration file
Training steps:
- Convert Udacity annotation format into YOLO format
- Set the following parameter in yolov3-tiny.cfg:
- set batch=24
- set subdivisions=8
- set filters=(3 + 5)*3 = 24
- set classes=3
- Train yolov3-tiny.weights with our dataset using Darknet library
- Train for ~5 days until average loss error < 0.06
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