Hailo's license detection network (tiny_yolov4_license_plates) is based on Tiny-YOLOv4 and was trained in-house using Darknet with a single class. It expects a single vehicle and can work under various weather and lighting conditions, on different vehicle types and numerous camera angles.
- Tiny-YOLOv4
- Number of parameters: 5.87M
- GMACS: 3.4
- Accuracy* : 73.45 mAP* Evaluated on internal dataset containing 5000 images
- RGB image with size of 416x416x3
- Image normalization occurs on-chip
- Two output tensors with sizes of 13x13x18 and 26x26x18.
- Each output contains 3 anchors that hold the following information:
- Bounding box coordinates ((x,y) centers, height, width)
- Box objectness confidence score
- Class probability confidence score
- The above 6 values per anchor are concatenated into the 18 output channels
The compiled network can be downloaded from here.
Use the following command to measure model performance on hailo’s HW:
hailortcli run2 set-net tiny_yolov4_license_plates.hef
A guide for finetuning the pre-trained model on a custom dataset can be found here