CosmeticDetectionYolo3

Introduction

A Cosmetic detector using YOLOv3 (Tensorflow backend) for cosmetic detection (ref: qqwweee/keras-yolo3)

Requirement

Environment

Python 3.6.8
Keras 2.2.4
Tensorflow 1.6.0
CUDA 9.0
cudnn 7.0
opencv 3.4.0

Libraries

colormath
Pillow
numpy
Keras
opencv-python
imgaug
matplotlib
omnis >= 0.0.8.4

Test data

Test data is under 'images/'

How to use:

1) Get the model

Step 1: Download the project:

git clone https://github.com/2013-11390/cosmeticDetectionYolo3.git

Step 2: Download YOLOv3 weights from YOLO website or yolov3.weights.

Step 3: Convert the Darknet YOLO model to a Keras model

python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5	# to get yolo.h5(model)

OR download the model yolo.h5 to model_data/ directory directly.

2) Test the model on coco dataset(original yolo model is trained on coco dataset)

Run YOLO detecion.

python yolo_video.py --model_path model_data/yolo.h5 --classes_path model_data/coco_classes.txt --image

3) Resnet 50 classification model

Step 1: Put cosmetic image in images folder by name 'cosmetic_dataset' Below 'cosmetic_dataset' you have to make directories divided by class names cosmetic_dataset

outputFalse outputTrue

Step 2: Train resnet 50 classification model

python trainResnet.py

Then you will get ImageClassification.h5 file in your folder

Step 3: Classify image by trained data

python ClassificationByResnet.py filepath

2013-11390/cosmeticDetectionYolo3