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YOLO-V3-Kitti

Train YOLO-V3-Kitti

DataSet: Kitti 2d object left color image

Data Split

  • Total:7481
  • Train:Test:Val: 8:1:1

original class

class_name count
DontCare 15782
Person_sitting 222
Pedestrian 0
Car 28742
Tram 511
Misc 973
Cyclist 1627
Truck 1094
Van 2914

Custom class

Class name (string in label file) Class ID (number in database) comment
Car 0
Truck 1 include [Truck, Van, Tram]
Person 2 include [Pedestrian, Person_sitting, cyclist]

Condition

Parameters value
batch 32
subdivisions 16
width 416
height 416
channels 3
momentum 0.9
decay 0.0005
filters 24
classes 3

Train log

Path: darknet/backup/

epoch weight_name
100 100.weights
200 200.weights
300 300.weights
400 400.weights
500 500.weights
600 600.weights
700 700.weights
800 800.weights
900 900.weights
10000 10000.weights
20000 20000.weights

Ref:

yolo v3

If you use YOLOv3 in your work please cite our paper! @article{yolov3, title={YOLOv3: An Incremental Improvement}, author={Redmon, Joseph and Farhadi, Ali}, journal = {arXiv}, year={2018} }

Kitti v3

Citation When using this dataset in your research, we will be happy if you cite us: @INPROCEEDINGS{Geiger2012CVPR, author = {Andreas Geiger and Philip Lenz and Raquel Urtasun}, title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012} }

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