In this post, I would like to share my practice with Facebook’s new Detectron2 package on macOS without GPU support for street view panoptic segmentation. If you want to create the following video by yourself, this post is all you need. This demo video clip is from my car’s dashcam footages from Preston, Melbourne. I used the PyTorch and Detectron2 to create this video with segmentation masks.
Phase 1: Problem assessment – Determine the problem’s characteristics.
What is an intelligent system?
The process of building Intelligent knowledge-based system has been called knowledge engineering since the 80s. It usually contains six phases: 1. Problem assessment; 2. Data and knowledge acquisition; 3. Development of a prototype system; 4. Development of a complete system; 5. Evaluation and revision of the system; 6. Integration and maintenance of the system .
Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany
Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.
When I am using TensorFlow on my MacBook Air, I always get annoyed by the warnings comes from nowhere, so I followed the documentation below to build TensorFlow sources into a TensorFlow binary and installed it successfully. In theory, this will make the TF running faster on my machine.
Here is the document:
If you are a Mac user, you could download the TF binary from here:
Then, you could use conda to initialize an environment with Python=3.6 and install TF by typing:
sudo pip install tensorflow-1.8.0-py2-none-any.whl
Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (Numpy, SciPy, Matplotlib, TensorFlow) it becomes a powerful environment for scientific computing and data analysis.
I start sharing my notebooks on learning Python and TensorFlow, here is my GitHub repository: Data_Science_Python.
Great news! PyTorch now is supporting Windows!
If you have a PC with suitable Nvidia graphics card and installed CUDA 9.0 and Anaconda, type the following commands;
conda install pytorch cuda90 -c pytorch
pip3 install torchvision
It is about 500 MB, so be patient!
Underline is the old post.
PyTorch is a deep learning framework that puts Python first. Currently, it only supports MacOS or Linux.
But, can we use it on WIN10 without changing the system/computer?
Yes, we can.
This post is a follow-up to my last post: A Taste of TensorFlow on My Android Phone.
You could download my compiled Apk file here and install on your android device (>Android 7.0) (22-May-2018).
Update: 25-May-2018, fixed the bug of object tracking function in TF Detect. PS: While running the activities, pressing the volume keys on your device will
toggle debug visualizations on/off, rendering additional info to the screen that
may be useful for development purposes.
- tensorflow_demo_debug.apk (My compiled version: 4 Apps ~ 109.8 MB );
- TfLiteCameraDemo.apk (official version: 1 App ~ 20.05 MB).
Feel free to let me know if there are any bugs 🙂
In the last post, I just tried the TensorFlow for object classification (TF Classify). This time I installed all four demos of the TensorFlow Mobile for Android according to this tutorial: TensorFlow Lite Demo for Android. They are awesome 😛
Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.
The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.
More details about the algorithm could be found in the following papers:
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution ;
- Instance Normalization: The Missing Ingredient for Fast Stylization.
If you could not download the papers, here are the Papers.
This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.
The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].
Moreover, I strongly recommend the formal paper Image Style Transfer Using Convolutional Neural Networks by the same authors published on CVPR-2016. This paper gives more details about how this method works. In short, the following two figures cover the idea behind the magic.