It’s an easy to use inference server for computer vision models. The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface). It’s backed by a bunch of component pieces: * a server (so you don’t have to reimplement things like image processing & prediction... - Source: Hacker News / 10 months ago

* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects. Source: over 1 year ago

For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com. - Source: Hacker News / over 1 year ago

Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers?ref=whoishiring1222 Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment. Over 100k engineers (including engineers from 2/3... - Source: Hacker News / over 1 year ago

Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com. Source: over 1 year ago

Maybe similar in spirit as idle games: I used to enjoy BoxCar2D where you just sit back and watch cars evolve using genetic algorithm. The original version[1] requires Flash, but looks like someone ported it to HTML5[2]: [1] http://boxcar2d.com/ [2] https://rednuht.org/genetic_cars_2/. - Source: Hacker News / about 2 months ago

Watch this for a while, it randomly generates a bunch of triangles and circles, the ones that make it furthest to the right have their code passed onto the next generation with random mutations. In almost no time you'll have a bunch of cars that are evolved specifically for the niche of that track. No guidance needed. Source: 6 months ago

You can actually produce a better analogy with a genetic algorithm. In the linked example, you can watch the evolution of simulated cars. Just take note that while the simulation is a good illustration of evolution, it's also, by necessity, vastly simplified. (I guess running for about 4 billion years would incur off-putting cloud costs.). Source: 6 months ago

This really doesn't require a nural net. You can run similar learning algorithms in your browser. Source: about 1 year ago

I learned about genetic algorithms: https://rednuht.org/genetic_cars_2/. Source: about 1 year ago