rocauc

8 ms·

1. 61.▲
   by rocauc 4y ago

   fantastic build. it’d be neat to complement this with collecting in-game statistics on which resources were collected by which player to further evaluate performance. I have a hunch the best players not only get access to the most resources
2. 62.▲
   by rocauc 4y ago

   Wonderful - and welcomed sequel to the Chuck Norris facts API: https://api.chucknorris.io/
3. 63.▲
   Show HN: Rickblocker, a computer vision approach to end rickrolling(github.com)

   14 points by rocauc 5y ago|4 comments
4. 64.▲
   What is CIPS? A primer on China’s version of SWIFT(twitter.com)

   7 points by rocauc 5y ago|0 comments
5. 65.▲
   by rocauc 5y ago

   you know, Jared Polis would likely appreciate this.
6. 66.▲
   by rocauc 5y ago

   Do you comprehend how the tool reduces labeling time in (4) and (5) (compared to labeling with eg CVAT) as the post claims?
7. 67.▲
   Prompt engineering: the magic words to using OpenAI's CLIP(blog.roboflow.com)

   1 points by rocauc 5y ago|0 comments
8. 68.▲
   How we built paint.wtf, an AI game with 150k submissions that judges your art(blog.roboflow.com)

   6 points by rocauc 6y ago|3 comments
9. 69.▲
   by rocauc 6y ago

   Ah, yeah, that one liner resonates with teams establishing their vision infra. Teams build production vision models in the span of an afternoon with better tools, e.g. one dev built this Mountain Dew bottle model during Super Bowl half time
10. 70.▲
    by rocauc 6y ago

    I was character limited - thank you
11. 71.▲
    by rocauc 6y ago

    We make developer tools for computer vision so engineers can create models without being machine learning experts: collect data, organize images, annotate, train, deploy, and improve.
12. 72.▲
    Using computer vision to help win $1M in Mountain Dew's Super Bowl contest(blog.roboflow.com)

    234 points by rocauc 6y ago|92 comments
13. 73.▲
    by rocauc 6y ago

    Makes a lot of sense, thanks. I'll need to dig deeper into Bayesian active learning techniques.
14. 74.▲
    by rocauc 6y ago

    Nice read. Can you shed some light on what you think are the most valuable methods for identifying high entropy examples for the model to learn faster? I'm familiar with Pool-Based Sampling, Stream-Based Selective Sampling, Membership
15. 75.▲
    by rocauc 6y ago

    Did you find the model to perform better / worse when the background varied? I see it's all wood table examples in the gifs.
16. 76.▲
    GitHub Repo Popularity vs. Company Growth(osschott.metabaseapp.com)

    2 points by rocauc 6y ago|0 comments
17. 77.▲
    Scaled-YOLOv4 Is Now the Best Model for Object Detection(blog.roboflow.com)

    3 points by rocauc 6y ago|0 comments
18. 78.▲
    Scaled-YOLOv4 Sets New Standard in Object Detection – Topping EfficientDet(blog.roboflow.com)

    5 points by rocauc 6y ago|0 comments
19. 79.▲
    by rocauc 6y ago

    Nice - how did you manage to store audio locally yet enable both parties to have access?
20. 80.▲
    by rocauc 6y ago

    Thank you very much for the kind words.
21. 81.▲
    by rocauc 6y ago

    Thanks for the copyedits - I've updated to "Joseph Redmon."
22. 82.▲
    by rocauc 6y ago

    I am guilty of using your dumb idea with satisfactory performance. I'd recommend EfficientNet (or one of its may variations) for an off-the-shelf SOTA classifier. https://paperswithcode.com/sota/image-classificatio
23. 83.▲
    PP-YOLO Surpasses YOLOv4 – State-of-the-art object detection techniques(blog.roboflow.ai)

    120 points by rocauc 6y ago|48 comments
24. 84.▲
    by rocauc 6y ago

    Really appreciate it; we had fun writing it. More of this to come.
25. 85.▲
    by rocauc 6y ago

    The 'hard costs' were the training time for each model ($125) and conducting inference on the valid set ($15) for each. Now, we had a few failed experiments like trying to run the COCO dataset through each tool. And none of this i
26. 86.▲
    Benchmarking the Major Cloud Vision AutoML Tools(blog.roboflow.ai)

    29 points by rocauc 6y ago|10 comments
27. 87.▲
    by rocauc 6y ago

    Thanks, I see the table. Are the source datasets available for creating additional benchmarks?
28. 88.▲
    by rocauc 6y ago

    Purpose-built, small, and fast models appears to be the inevitable evolution for computer vision. Where can the "Easy Set, "Medium Set, and "Hard Set" evaluations referenced in the "Wider Face Val" be found?
29. 89.▲
    by rocauc 6y ago

    Thanks for your followup thoughts. Pleased the conversation has evolved to focus on architecture and performance rather than naming alone. re: GitHub comment deletion - We determined we should engage when we have easier to reproduce results
30. 90.▲
    Responding to the Controversy about YOLOv5(blog.roboflow.ai)

    7 points by rocauc 6y ago|4 comments