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YOLOv3

Introduction

@misc{redmon2018yolov3,
    title={YOLOv3: An Incremental Improvement},
    author={Joseph Redmon and Ali Farhadi},
    year={2018},
    eprint={1804.02767},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Results and Models

Backbone Scale Lr schd Mem (GB) Inf time (fps) box AP Config Download
DarkNet-53 320 273e 2.7 63.9 27.9 config model | log
DarkNet-53 416 273e 3.8 61.2 30.9 config model | log
DarkNet-53 608 273e 7.4 48.1 33.7 config model | log

Mixed Precision Training

We also train YOLOv3 with mixed precision training.

Backbone Scale Lr schd Mem (GB) Inf time (fps) box AP Config Download
DarkNet-53 608 273e 4.7 48.1 33.8 config model | log

Lightweight models

Backbone Scale Lr schd Mem (GB) Inf time (fps) box AP Config Download
MobileNetV2 416 300e 5.3 23.9 config model | log
MobileNetV2 320 300e 3.2 22.2 config model | log

Notice: We reduce the number of channels to 96 in both head and neck. It can reduce the flops and parameters, which makes these models more suitable for edge devices.

Credit

This implementation originates from the project of Haoyu Wu(@wuhy08) at Western Digital.