The code for all this is available here.

Have you ever wondered what a complex vue js application really looks like?

Today I’m happy to take a deep dive into the architecture of Diffgram’s Open Annotation UI. This is intended for people who are curious about VueJS at scale.

First let’s set the stage. This is a project that started nearly 3 years ago. There are issues we are aware of — and many we probably aren’t. If you happen to spot something please open an issue here.

Here are some examples of what the screens can look like.

Over 30 options considered for this Mega List. Map of Landscape and Buying Guide Included. For Training Data, Data Annotation, and Deep Learning.

Introduction — The Best Data Annotation Tools

We all know Artificial Intelligence (AI) is taking the world by storm. While the technology has significant limitations — it’s maturing rapidly and it’s absolutely critical to be up on the latest trends of it.

Here I walk through the top Artificial Intelligence Data Annotation platforms. If you are an executive, technical person new to the area, or just curious, this article is for you!

Let’s dive in! :)

Map of Training Data Landscape 2021

This map covers the top open source and…

On April 23 I posted this article on the best data annotation for machine learning. Their response?

They sent an inflammatory letter from their law firm, that implies the threat of legal action under the Lanham Act, which is Federal Court.

“I caution you that knowingly making false statements of this sort can give rise to liability for defamation and for violations of section 43(a) of the Lanham Act. Given the seriousness…” —Labelbox law firm from letter

To us — Diffgram. A self funded, pre-seed startup. So why are they doing it? What threat could our small team be?


I’m really excited to introduce Userscripts.

What if you could use any Machine Learning model to improve training data? For free? Introducing Userscripts.

What is it?

Userscripts are JavaScript (JS). The high level idea is that we can build custom functions inside Diffgram. Here are some examples of what you can achieve:

Want to see the code?

Get the full code

What’s the big deal?

Free, Freaky Powerful, and Fast!!

This is a real game changing feature because now it’s free to run these models as often as need. (Becuase it runs on the local computer).

It’s powerful because it’s actually code. You can…

Cloud made getting and returning compute resources as easy as turning a water faucet.

Diffgram Pros does the same for this type of professional human labor.

Rapidly ramp up and down what you need when you need it, all from a 100% digital interface.

The trend for Artificial Intelligence (AI) systems is for “real” supervision — not mindless drones drawing boxes.

This means there is a growing need for professionals — subject matter experts.

From university students, to technicians, to full time professionals — humans are the flexible membrane that makes AI automations work.

Introducing AI data by Pros on…

Subject Matter Experts

Annotations are a very human endeavor. It’s a high touch, high usage system, where annotators can be spending many hours per day on the system. This means a system needs to be performant to the level of say a word processor.

Time to Market

Even if a team and support budget is put together, it will take years to build an effective, tested, and scalable system. As with software engineering, the durable version is at least 10x longer to create than the prototype. While Diffgram is still new, the product trends towards the durable status with over 900,000 files created in over 1,000…

Let’s start with why we exist — why we are passionate about Diffgram.

We believe Artificial Intelligence (AI) should be in every system because it automates knowledge tasks — leading to more creative work and multiplying the effectiveness of rare knowledge. Without supervision, AI Deep Learning systems don’t work. We create software for AI supervision.

Improving Training Data simultaneously helps improve Health care by extending doctors reach, improves Agriculture harvesting to feed more people, and extends top sports coaches knowledge to aspiring players. The faster we can improve and adopt Training Data the faster the adoption of these applications.

Want to be part of this journey? Take action

Once upon a time I was working with digital marketing. There was a big meeting. How do I know it was a big meeting? Well we had to fly to the nearest major city (Calgary) to participate. Why were we there? While despite being located in one of the smallest metro areas in Canada, the store was actually THE most successful of 452 stores nationwide. By a long shot. Company reps would come to the store and joke we had more trucks on hand then the factory — so much inventory was needed since we sold so many! …

I’m working on categorizing problem solving methods. If you see one I have missed please comment!

Order is for ease of reference only and does not imply any other meaning.

  1. Divide problem into two parts until solved (divide and conquer, recursively)
  2. Visualize problem
  3. Find or use an Example
  4. Inverse / counter point
  5. Guess and check, especially around testing assumptions / similar examples
  6. Search for existing knowledge
  7. Reference generally related material / nearby material
  8. Collaboration
  9. Weighting / voting
  10. Thought experiment
  11. Iteration (on existing progress)
  12. Naive, iterate through all known possible angles
  13. Create additional constraints / ignore part of the problem
  14. Do the opposite of what you have been doing

Some of these may be overly broad. For example Collaboration is a catch all for almost anything involving other people.

Exploring more complex forms of annotation.

In supervised Deep Learning a starting point is a single label, such as “Vehicle”.

However, real world input normally has more detail to it. For example this white vehicle is blocked (or Occluded) by the red vehicle. The light blue vehicle in the bottom right is out of the frame so it’s considered, Truncated.

Image from public domain.

We may wish to further specify a percentage, for example we may say the white vehicle is 41–60% occluded. The Blue vehicle appears to be 81–100% truncated.

Anthony Sarkis

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