Use Case and High-Level Description

This is a YOLO V3 network finetuned for Person/Vehicle/Bike detection for security surveillance applications. It works in a variety of scenes and weather/lighting conditions.

Yolo V3 is a real-time object detection model implemented with Keras* from this repository and converted to TensorFlow* framework.

This model was pretrained on COCO* dataset with 80 classes and then finetuned for Person/Vehicle/Bike detection.



Metric Value
Mean Average Precision (mAP) 48.89%
AP people 58.94%
AP vehicles 62.05%
AP bikes/motorcycles 25.66%
GFlops 65.98
MParams 61.92
Source framework Keras*

Average Precision (AP) is defined as an area under the precision/recall curve.

Validation dataset consists of 34757 images from various scenes and includes:

Type of object Number of bounding boxes
Vehicle 229503
Pedestrian 240009
Bike/Motorcycle 62643

Similarly, training dataset has 17084 images with:

Type of object Number of bounding boxes
Vehicle 121111
Pedestrian 119546
Bike/Motorcycle 30220


Name: image_input , shape: [1x3x416x416] - An input image in the format [BxCxHxW], where

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

Expected color order: BGR.


  1. The array of detection summary info, name - conv2d_58/Conv2D/YoloRegion, shape - 1,255,13,13. The anchor values are 116,90, 156,198, 373,326.
  2. The array of detection summary info, name - conv2d_66/Conv2D/YoloRegion, shape - 1,255,26,26. The anchor values are 30,61, 62,45, 59,119.
  3. The array of detection summary info, name - conv2d_74/Conv2D/YoloRegion, shape - 1,255,52,52. The anchor values are 10,13, 16,30, 33,23.

For each of the arrays the output format is B,N*85,Cx,Cy, where

  • B - batch size
  • N - number of detection boxes for cell
  • Cx, Cy - cell index

Detection box has format [x,y,h,w,box_score,class_no_1, ..., class_no_80], where:

  • (x,y) - coordinates of box center relative to the cell
  • h,w - raw height and width of box, apply exponential function and multiply them by the corresponding anchors to get the absolute height and width values
  • box_score - confidence of detection box in [0,1] range
  • class_no_1,...,class_no_80 - probability distribution over the classes in the [0,1] range, multiply them by the confidence value box_score to get confidence of each class

Since the model is finetuned on person/vehicle/bike detection dataset, it returns non-zero scores for the following classes:

  • person - the first class score
  • non-vehicle (bike/motorcycle) - the second class score
  • vehicle - the third class score Note that the indexes of these 3 classes are aligned with the indexes of the classes person, bike, and car in the original COCO* dataset. Also note that the model returns class scores for all 80 COCO classes for backward compatibility with the original Yolo V3.

Legal information

[*] Other names and brands may be claimed as the property of others.