Official implementation of Improving Tiny YOLO with Fewer Model Parameters, IEEE BigMM 2021
With the rapid development of convolutional neural networks (CNNs), there are a variety of techniques that can improve existing CNN models, including attention mechanisms, activation functions, and data augmentation. However, integrating these techniques can lead to a significant increase in the number of parameters and FLOPs. Here, we integrated Efficient Channel Attention Net(ECA-Net), Mish activation function, All Convolutional Net (ALL-CNN), and a twin detection head architecture into YOLOv4-tiny, yielding an AP 50 of 44.2% on the MS COCO 2017 dataset. The proposed Attention ALL-CNN Twin Head YOLO (A 2 -YOLO) outperforms the original YOLOv4-tiny on the same dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO
This project is based on WongKinYiu/PyTorch_YOLOv4 u3_preview branch with some modification
The AA-YOLO architecture file is at AA-YOLO-twin-head.cfg
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You can use this project to train 416x416 YOLOv4-tiny. GitHub Issues
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View our experiment environment infos here
Expand Development Log
使用test.py針對Cross-Stitch架構進行AP測試時，必須到test.py的第43行將model.fuse()功能關閉
#model.fuse()
新增detect.py計算FPS功能 (detect.py的第8行、138~140行、171行)
# line 8
FPS_avg = []
# line 138~140
from statistics import mean
print('%sDone. (FPS:%.1f)' % (s, 1/(t2 - t1)))
FPS_avg.append(1/(t2-t1))
# line 171
print('Avg FPS: (%.1f)' % (mean(FPS_avg)))
解決test.py執行時遇到numpy必須要1.17版本及No module named 'pycocotools'的方法:
#移除所有numpy
pip uninstall numpy
#安裝1.17版本numpy
pip install numpy==1.17
#安裝pycocotools
pip install pycocotools
#執行test.py
python test.py --data coco2017.data --cfg yolov4-tiny.cfg --weights yolov4-tiny.pt --img 416 --augment新增支援yolov4.conv.137的Pre-trained weight功能(在model.py第456~456行)
elif file == 'yolov4.conv.137':
cutoff = 137
新增YOLOv4-tiny的RouteGroup功能
Feature-request: YOLOv4-tiny #1350
新增ReLU6(在utils/layers.py)、ReLU(在utils/layers.py)、DepthWise Convolution(在models.py)、ShuffleNetUnit(在models.py)
如何透過u5版本的yaml檔案進行backbone修改？
Yolov4 with Efficientnet b0-b7 Backbone
Yolov4 with MobileNet V2/V3 Backbone
Yolov4 with Resnext50/ SE-Resnet50 Backbones
新增以下四篇Paper的程式碼(在models.py、utils/layers.py)
SE Block paper : arxiv.org/abs/1709.01507
CBAM Block paper : arxiv.org/abs/1807.06521
ECA Block paper : arxiv.org/abs/1910.03151
Funnel Activation for Visual Recognition : arxiv.org/abs/2007.11824
修改了PyTorch_YOLOv4的u3_preview當中，models.py第355行(支援pre-trained weight)、train.py第67行(支援32倍數的解析度)、dataset.py第262及267行(處理dataset相對路徑的問題) models.py line 355
def load_darknet_weights(self, weights, cutoff=-1):
# Parses and loads the weights stored in 'weights'
# Establish cutoffs (load layers between 0 and cutoff. if cutoff = -1 all are loaded)
file = Path(weights).name
if file == 'darknet53.conv.74':
cutoff = 75
elif file == 'yolov3-tiny.conv.15':
cutoff = 15
elif file == 'yolov4-tiny.conv.29':
cutoff = 29
.
.
.
.train.py line 67
gs = 32 # (pixels) grid size # 原本gs = 64改成32
assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)utils/dataset.py line 262 and 267
class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_images=False, single_cls=False):
path = str(Path(path)) # os-agnostic
parent = str(Path(path).parent) + os.sep # add this
assert os.path.isfile(path), 'File not found %s. See %s' % (path, help_url)
with open(path, 'r') as f:
self.img_files = [x.replace('/', os.sep) for x in f.read().splitlines() # os-agnostic
if os.path.splitext(x)[-1].lower() in img_formats]
self.img_files = [x.replace('./', parent) if x.startswith('./') else x for x in self.img_files] # add thisOur environment setting on https://www.twcc.ai/
NVIDIA-SMI 450.80.02 Driver Version: 450.80.02 CUDA Version: 11.0
python 3.6.9
PyTorch 1.6.0
Torchvision 0.7.0
tensorflow-20.06-tf2-py3:tag1614508696283
numpy 1.17.0
2021/03/31 update:
Be careful to use NAS (Network Attached Storage) to store the coco dataset, it might slow down the tranining speed. Store your dataset locally.
# Install mish-cuda if you want to use fewer GPU memory during training (from 12G -> 8G)
pip install git+https://github.com/thomasbrandon/mish-cuda/
pip install -r requirements.txt
sh data/get_coco.sh
CUDA_VISIBLE_DEVICES=0 python train.py --data coco2017.data --cfg AA-YOLO-twin-head.cfg --weights 'yolov4-tiny.conv.29' --name yolov4-tiny --img 416
CUDA_VISIBLE_DEVICES=0 python test.py --data coco2017.data --cfg AA-YOLO-twin-head.cfg --weights yolov4-tiny.pt --img 416 --augment
CUDA_VISIBLE_DEVICES=0 python detect.py --weights AA-YOLO-twin-head.pt --img 416 --source file.mp4 # video
file.jpg # image
python -c from utils import utils;utils.plot_results().
@inproceedings{9643269,
title={Improving Tiny YOLO with Fewer Model Parameters},
author={Liu, Yanwei and Ma, Ching-Wen},
booktitle={2021 IEEE Seventh International Conference on Multimedia Big Data (BigMM)},
pages={61-64},
year={2021},
doi={10.1109/BigMM52142.2021.00017}}