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jayeb
jayeb commented Jun 19, 2017

If an explicit sizes attribute is declare on an image element, we should probably take that size (or sizes) into account when generating our srcset. For example, this image

<img size="120px" ix-path="..." />

should probably end up with a srcset list that includes only URLs with widths in multiples of 120px, rather than the typical "100, 200, 300, 320" list.

opattison
opattison commented Feb 5, 2016

I’m not sure if this is a documentation issue or a specific configuration that I have that works differently. I ran into a brief stumbling block when configuring the gems in _config.yml as the documentation suggested. I’m using bundle exec in a conventional way.

When running $ JEKYLL_ENV=production bundle exec jekyll build:

**Case: error when config gems set to [jekyll/imgix] or `jekyl

The eager-loading for image files on the web page that loads the files according to your plan. This differs from the lazy-loading, for example, this can be used to avoid that the user waits for the loading.

  • Updated Mar 2, 2018
  • JavaScript
eboto
eboto commented Jul 8, 2016

Unlike the javascript API, the java API forces you to double url-encode the source URL path provided to createURL.

Steps to reproduce:

  • Have a file located at https://my.domain/project:1/blah.jpg
  • URL-encode it once, so that it becomes https://my.domain/project%3A1/blah.jpg
  • Use HTTPBuilder.createURL to create an imgix URL against your imgix domain.
  • Attempt to curl that URL, see that i

Keeping roads in a good condition is vital to safe driving. To monitor the degradation of road conditions is one of the important component in transportation maintenance which is labor intensive and requires domain expertise. Automatic detection of road damage is an important task in transportation maintenance for driving safety assurance. The intensity of damage and complexity of the background, makes this process a challenging task. A deep-learning based methodology for damage detection is proposed in this project after being inspired by recent success on applying Deep- learning in Computer Sciences. A dataset of 9,053 images is taken with the help of a low cost smart phone and a quantitative evaluation is conducted, which in turn demonstrates that the superior damage detection performance using deep-learning methods perform extremely well when compared with features extracted with existing hand-craft methods. Using convolutional neural networks to train the damage detection model with our dataset, we use the state-of-the-art object detection method, and compute the accuracy and runtime speed on a GPU server. At the end, we show that the type of damage can be distinguished into eight types with acceptable accuracy by applying the proposed object detection method.

  • Updated Sep 12, 2018
  • Jupyter Notebook

A collection of python implementations using SWIG, Instant, F2PY... Optimization like Least Squares Levenberg-Marquardt. Boundary Value problem solvers. Integration Simpson/Trapezoidal. Interpolation like Cubic spline. Tridiagonal/pentadiagonal system of equations solver. Linear algebra like Matrix inversion (Gauss-Jordan) and much more

  • Updated Aug 11, 2018
  • C

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