[LEADERG AI ZOO] Jupyter-Image-Object-Detection-FasterRCNN-Keras

Language:

繁體中文

 

 

[Introduction]

 

Use FasterRCNN for object detection, which can be applied to factory defect detection, medical image analysis, biological image analysis, industrial safety image analysis, and mask image analysis.

 

 

[Instruction]

 

The solution process is:

Annotate images -> training -> inference

 

1. 1_annotation_pascal_voc_xml.ipynb

After running, open the web page for image annotation.

parameter:

In # parameters, --port 8801 is the port occupied by the webpage. If the user 8801 is occupied, please change the port value by yourself.

 

2. 2_delete_log.ipynb

Delete the log folder.

 

3. 3_train.ipynb

Start training.

If you don't use the tensorboard , you can skip points 2, 4, and 5.

 

4. 4_kill_tensorboard.ipynb

Before using tensorboard to see the loss value, close tensorboard first.

 

5. 5_tensorboard.ipynb

During training, you can run 4 and 5 points to see the training status, but you must execute 2_delete_log.ipynb before training.

 

6. 6_inference.ipynb

Inferring a single image.

parameter:

-p is the path to infer the image.

 

FastRCNN.png

 


Recommended Article

1.
LEADERG AOI VISION - AI Image Analysis Software (No Code, Windows User Interface, Deep Learning, Machine Learning)

2.
LEADERG AI ZOO - AI Sample Code Software (Save 90% of Development Time, Low Code, Jupyter Lab User Interface, Deep Learning, Machine Learning, Big Data)

3.
LEADERG AI Computer (Deep Learning, PC, workstation, server, NVIDIA GPU, TESLA A100, RTX-3090-24G, Intel VPU, GPU Server)

4.
LEADERG AI Education

Thanks for our customers

Taiwan University, Tsing Hua University, Yang Ming Chiao Tung University, Cheng Kung University, Taipei Medical University, Taipei University of Nursing and Health Sciences, National Chung Hsing University, Chi Nan University, Ilan University, United University, Defence University, Military Academy, Naval Academy, Feng Chia University, Chang Gung University, I-Shou University, Shih Chien University, Taiwan University of Science and Technology, Taichung University of Science and Technology, Yunlin University of Science and Technology, Chin-Yi University of Science, Formosa University, Pintung University of Science and Technology, Kaohsiung University of Science and Technology, Chaoyang University of Technology, Ming Chi University of Technology, Southern Taiwan University of Science and Technology, China University of Technology, Gushan Senior High School, Taipei Veterans General Hospital, Chang Gung Medical Foundation, Tzu Chi Medical Foundation, E-Da Hospital, Industry Technology Research Institute, Institute for Information Industry, Chung-Shan Institute of Science and Technology, Armaments Bureau, Ministry of Justice Investigation Bureau, Institute of Nuclear Energy Research, Endemic Species Research Institute, Institute of Labor, Occupational Safety And Health, Metal Industries Research & Development Centre, Taiwan Instrument Research Institute, Automotive Research & Testing Center, Taiwan Water Corporation, Taiwan Semiconductor Manufacturing Co., Ltd., United Microelectronics Corp., Nanya Technology, Winbond Electronics Corp., Xintec Inc., Arima Lasers Corporation, AU Optronics Corporation, Innolux Corporation, HannStar Display Corporation, Formosa Plastics Group., Formosa Technologies Corporation, Nan Ya Plastics Corp., Formosa Chemicals & Fibre Corporation, Chinese Petroleum Corporation, Logitech, ELAN Microelectronics Corp., Lextar Electronics Corporation, Darfon Electronics Corp., WPG Holdings, Mirle Automation Corporation, Symtek Automation Asia Co., Ltd, ChipMOS Technologies Inc., Dynapack International Technology Corporation, Primax Electronics Ltd., Feng Hsin Steel, China Ecotek, Grade Upon Technology Corp., AAEON Technology Inc., Stark Technology, Inc., Horng Terng Automation Co., Ltd., Zhen Ding Technology Holding Ltd, Boardtek Electronics Corporation, MiTAC International Corporation, Allion Labs, Inc., Sound Land Corp., Hong Hu Tech, etc.