README.md

yolov3_fire_detection

part 1. Introduction

Implementation of YOLO v3 object detector in Tensorflow for Fire and Smoke detection. The full details are in this paper. In this project we cover several segments as follows:

• YOLO v3 architecture
• Training tensorflow-yolov3 with GIOU loss function
• Basic working demo
• Training pipeline
• Detection result

YOLO paper is quick hard to understand, along side that paper. This repo enables you to have a quick understanding of YOLO Algorithmn.

part2. Fire and Smoke detection demo

$ python demo.py

part3. Train on your own dataset

Two files are required as follows:

• dataset.txt:

xxx/xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20 
xxx/xxx.jpg 48,240,195,371,11 8,12,352,498,14
# image_path x_min, y_min, x_max, y_max, class_id  x_min, y_min ,..., class_id 

• class.names:

fire
smoke

Then edit your ./core/config.py to make some necessary configurations

__C.YOLO.CLASSES                = "./data/classes/Fire.names"
__C.TRAIN.ANNOT_PATH            = "./data/my_data/fire_train.txt"
__C.TEST.ANNOT_PATH             = "./data/my_data/fire_val.txt"

Here are two kinds of training method:

(1) train from scratch:

$ python train.py
$ tensorboard --logdir ./data

(2) train from COCO weights(recommend):

$ cd checkpoint
$ wget https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3_coco.tar.gz
$ tar -xvf yolov3_coco.tar.gz
$ cd ..
$ python convert_weight.py --train_from_coco
$ python train.py

how to test and evaluate it ?

$ python evaluate.py
$ cd mAP
$ python main.py -na

part 4. Detection result

part 5. Pre-train model

The pre-trained model can be download in baidu pan. 提取码：tvm6