- TensorFlow Version: 1.0.1
- Written by Korean Version
This is a Pi-based robot to implement visual recognition(by Inception V3). The TensorFlow-Powered vision can recognize many objects such as people, car, bus, fruits, and so on.
- Hardware: Raspberry-Pi2, Sony PS3 Eye Camera(Available to use Logitech C270 USB camera with Raspberry Pi)
- Software: TensorFlow(v1.0.1), Jupyter-Notebook
I was so curious about excellence of the image recognition with TensorFlow on Raspberry Pi. Also, the Jupyter notebook is very convenient to instantly code as a quick prototype. So, in terms of error rate of the image classification, Inception V3(3.46%) is more excellent than human(5.1%) whereas raspberry pi’s processing speed is very slow compare to my laptop.
- Schematic diagram of Inception-v3
- Install Webcam driver on your Rapsberry Pi.
sudo apt-get install fswebcam
- Test your webcam.
- TensorFlow (V1.0.1): How To Install TensorFlow on Raspberry Pi
- Jupyter-Notebook: How To Install Jupyter-Notebook on Raspberry Pi
- You should install both TensorFlow(v1.0.1) and Jupyter notebook on your Raspberry Pi.
- First, clone the TensorFlow-Powered_Robot_Vision git repository here. This can be accomplished by:
cd /home/pi/Documents git clone https://github.com/leehaesung/TensorFlow-Powered_Robot_Vision.git
next, cd into the newly created directory:
Drive your jupyter notebook on your Raspberry Pi.
The pre-trained data(inception_v3.ckpt) will automatically download when driving the Jupyter notebook. (Where: /pi/home/Documents/datasets/inception)
- inception_v3.ckpt (When implementing the jupyter notebook)
- Wow! The result is really awessome!!
- Rethinking the Inception Architecture for Computer Vision (Paper)
- Image Recognition::Tensorflow.org (Web)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (Book)
- Train your own image classifier with Inception in TensorFlow (Blog)
- “Large-Scale Deep Learning for Building Intelligent Computer Systems,” a Keynote Presentation from Google