choiseoungho · October 7, 2020 10:46
diff --git a/Yolo serize b/Yolo serize
 Yolo v1 - v3 Review
 1. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once, Unified, Real-Time Object Detection,” 
 2. J. Redmon, and A. Farhadi “YOLO9000: Better, Faster, Stronger” arXiv:1612.08242v1 [cs.CV] 25 Dec 2016.
 3. P. Zhang, Y. Zhong, and X. Li, “SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications,” arXiv:1907.11093, 2019.

 Yolov3 Acceleator Inference
 - Adopted : J. Choi, S. Venkataramani, V. Srinivasan, K. Gopalakrishnan, Z. Wang, P. Chuang, “ACCURATE AND EFFICIENT 2-BIT QUANTIZED NEURAL NETWORKS,”
 - Hardware Accelerators for Machine Learning공부 후 이거 적용 https://cs217.stanford.edu/

 https://www.ibm.com/blogs/research/2019/04/2-bit-precision/
 https://www.sysml.cc/doc/2019/168.pdf
	Yolo v1 - v3 Review
	1. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once, Unified, Real-Time Object Detection,”
	2. J. Redmon, and A. Farhadi “YOLO9000: Better, Faster, Stronger” arXiv:1612.08242v1 [cs.CV] 25 Dec 2016.
	3. P. Zhang, Y. Zhong, and X. Li, “SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications,” arXiv:1907.11093, 2019.

	Yolov3 Acceleator Inference
	- Adopted : J. Choi, S. Venkataramani, V. Srinivasan, K. Gopalakrishnan, Z. Wang, P. Chuang, “ACCURATE AND EFFICIENT 2-BIT QUANTIZED NEURAL NETWORKS,”
	- Hardware Accelerators for Machine Learning공부 후 이거 적용 https://cs217.stanford.edu/

	https://www.ibm.com/blogs/research/2019/04/2-bit-precision/
	https://www.sysml.cc/doc/2019/168.pdf