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README.md | 1 year ago | |
metafile.yml | 1 year ago | |
yolov3_d53_320_273e_coco.py | 1 year ago | |
yolov3_d53_fp16_mstrain-608_273e_coco.py | 1 year ago | |
yolov3_d53_mstrain-416_273e_coco.py | 1 year ago | |
yolov3_d53_mstrain-608_273e_coco.py | 1 year ago | |
yolov3_mobilenetv2_320_300e_coco.py | 1 year ago | |
yolov3_mobilenetv2_mstrain-416_300e_coco.py | 1 year ago |
@misc{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Joseph Redmon and Ali Farhadi},
year={2018},
eprint={1804.02767},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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 |
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 |
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.
This implementation originates from the project of Haoyu Wu(@wuhy08) at Western Digital.
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