Introduction

This repository contains my master's (ongoing) work on model compression techniques at YOLOv3. It is freely available for redistribution under the GPL-3.0 license. This repository is based on YOLOv3 Ultralytics.

Currently evaluated approaches:

Lottery Tickets Hypothesis (Iterative Magnitude based Pruning)
Continuous Sparsification (Iterative Gradient based Pruning)
Knowledge Distillation (classical approach)
Generative Adversarial Network (GAN) based Knowledge Distillation
Neural Architecture Search (NAS) from MobileNet V3
NAS from YOLO Nano

Requirements

Python 3.7 or later with all of the pip install -U -r requirements.txt packages including:

numpy = 1.19 (version 1.18 raises bugs on COCOAPI)
torch >= 1.7
opencv-python
Pillow
THOP to count the MACs

Other Details

I am now focused on completing my master's (scheduled for March, 2020). With this task completed, I will bring you the final results of the work and examples of how to run this repository. Basically, run

train.py to perform a normal training,
prune.py to perform pruning with LTH or CS, depending on the params
my_kd.py to perform classical KD with YOLOv3 and YOLO Mobile (model of my own) or YOLO Nano
my_kd_gan.py to perform my adapted GAN based KD In utils/my_utils.py, you can see the argument parser, to see all the available parameters

Results

Pascal VOC 2007 test set

Model	Training	mAP	Final Params	MACs	Storage (MB)
YOLOv3-Tiny	Default	0.379 ± 0.003	8, 713, 766	2, 753, 665, 551	33.29
YOLOv3	Default	0.547 ± 0.012	61, 626, 049	32, 829, 119, 167	235.44
YOLO Nano	Default	0.385 ± 0.007	2, 890, 527	2, 082, 423, 381	11.38
YOLOv3-Mobile	Default	0.009 ± 0.008	4, 395, 985	1, 419, 864, 487	17.59
YOLOv3	LTH Local	0.549 ± 0.009	6, 331, 150 ± 1	3, 468, 547, 347 ± 278	118.26
YOLOv3	LTH Global	0.561 ± 0.009	6, 331, 114 ± 1	8, 796, 051, 025 ± 225, 877, 824	118.26
YOLOv3	CS 1 It	0.442 ± 0.010	740, 072 ± 12, 161	1, 137, 839, 381 ± 44, 191, 983	11.618 ± 0.23
YOLOv3	CS 3 It	0.316 ± 0.015	421, 721 ± 3, 544	618, 724, 616 ± 20, 611, 379	5.544 ± 0.07
YOLO Nano_leaky	KD fts 79	0.421 ± 0.007	2, 890, 527	2, 098, 305, 681	11.38
YOLO Nano_leaky	KD fts 36, 61	0.408 ± 0.008	2, 890, 527	2, 098, 305, 681	11.38
YOLO Mobile_leaky	KD fts 91	0.253 ± 0.023	4, 395, 985	1, 458, 910, 247	17.59
YOLO Mobile_leaky	KD fts 36, 91	0.244 ± 0.010	4, 395, 985	1, 458, 910, 247	17.59
YOLO Nano_leaky	KD GAN	0.395 ± 0.012	2, 890, 527	2, 098, 305, 681	11.38
YOLO Mobile_leaky	KD GAN	0.311 ± 0.006	4, 395, 985	1, 458, 910, 247	17.59

ExDark test set

Model	Training	mAP	Final Params	MAC	Storage (MB)
YOLOv3-Tiny	Default	0.287 ± 0.020	8, 695, 286	2, 747, 415, 255	33.22
YOLOv3	Default	0.453 ± 0.017	61, 582, 969	32, 799, 960, 583	235.27
YOLO Nano	Default	0.242 ± 0.013	2, 872, 743	2, 071, 460, 013	11.31
YOLOv3-Mobile	Default	0.000 ± 0.000	4, 390, 537	1, 416, 145, 135	17.57
YOLOv3	LTH Local	0.461 ± 0.012	6, 288, 070 ± 1	3, 439, 388, 763 ± 278	118.1
YOLOv3	LTH Global	0.471 ± 0.018	6, 288, 035 ± 1	9, 665, 082, 014 ± 288, 425, 550	118.09
YOLOv3	CS 1 It	0.294 ± 0.012	525, 823 ± 7, 684	941, 520, 024 ± 58, 158, 009	8.188 ± 0.149
YOLOv3	CS 3 It	0.139 ± 0.004	290, 746 ± 1, 638	505, 248, 788 ± 15, 650, 702	3.702 ± 0.032
YOLO Nano_leaky	KD fts 79	0.303 ± 0.008	2, 872, 743	2, 087, 342, 313	11.31
YOLO Nano_leaky	KD fts 61, 91	0.295 ± 0.010	2, 872, 743	2, 087, 342, 313	11.31
YOLO Mobile_leaky	KD fts 91	0.113 ± 0.021	4, 390, 537	1, 455, 190, 895	17.57
YOLO Mobile_leaky	KD fts 36, 91	0.107 ± 0.005	4, 390, 537	1, 455, 190, 895	17.57
YOLO Nano_leaky	KD GAN	0.254 ± 0.007	2, 872, 743	2, 087, 342, 313	11.31
YOLO Mobile_leaky	KD GAN	0.157 ± 0.005	4, 390, 537	1, 455, 190, 895	17.57

AndreydeAguiarSalvi / yolo_compression

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