YOLOv5: State-of-the-art object detection at 140 FPS • Hacker News Board

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#41

Post by normanluhrmann » Wed, Jun 10, 2020, 6:12 PM UTC

yodon wrote: post #20

WanderPanda wrote: post #12

Earlier quoted context omitted.

f(x)=c, zero size, infinite fps. You should also take some accuracy metric into account ;)

> "Similarly accurate"

MS COCO looks to be improved even overall.

OT: the stats above should be part of the PyTorch marketing material, indeed impressive

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#42

Post by travisporter » Wed, Jun 10, 2020, 6:16 PM UTC

qchris wrote: post #22

If anyone's interested in the direct GitHub link to the repository: https://github.com/ultralytics/yolov5

Hm on this page it has something written in an eastern language under YOLO, https://github.com/ultralytics says Madrid, Spain, but then they say "Ultralytics is a U.S.-based particle physics and AI startup"

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#43

Post by wiz21c » Wed, Jun 10, 2020, 6:22 PM UTC

lm28469 wrote: post #30

jcims wrote: post #23

Earlier quoted context omitted.

>Facial recognition has very few applications outside of total surveillance. That's not really for you to decide, is it? You're absolutely free to have that opinion of course. >We should not respect those who lend it their time and effort. Also your choice of course. Facial recognition is essentially a light integration of powerful underlying technologies. Should 'we' ostracize those working on machine learning, comp…

The question is always the same: is every technical/scientific progress desirable ? But it seems that this question isn't asked anymore, "move fast and break things" am I right ? I'm much more worried about people using your arguments to try and shut down the discussion than people trying to open the debate, because once the mass surveillance/face recognition mass adoption pandora's box is open there won't be any way…

>>> When I see predator drones and FBI stingray planes above every major us cities during protests

Can you provide evidence for this ? Not that I doubt it, but if I want to tell other people that story, I must have evidence to be believed :-)

edit: ah of course, 30 seconds of duckduckgo just provide the info I need : https://thehill.com/homenews/house/501445-democrats-press-dh...

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#44

Post by franciscop » Wed, Jun 10, 2020, 6:24 PM UTC

I am very interested on loading YOLO into a Raspberry Pi + Coral.ai, anyone knows a good tutorial on how to get started? I tried before and with Darknet it was not easy at all, but now with pytorch there seem to be ways of loading that into Coral. I am familiar with Raspberry Pi dev, but not much with ML or TPUs, so I think it'd be mostly a tutorial on bridging the different technologies.

(might need to wait a couple of months since this was just released)

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#45

Post by foota » Wed, Jun 10, 2020, 6:26 PM UTC

sillysaurusx wrote: post #26

sillysaurusx wrote: post #18

We made a site that lets you collaboratively tag a bunch of images, called tagpls.com. For example, users decided to re-tag imagenet for fun: https://twitter.com/theshawwn/status/1262535747975868418 And the tags ended up being hilarious: https://pbs.twimg.com/media/EYXRzDAUwAMjXIG?format=jpg&name=... (I'm particularly fond of https://i.imgur.com/ZMz2yUc.png ) The data is freely available via API: https://www.tagpls.c…

Well, that didn't take long – our API endpoint keeled over. Luckily, you can fetch all the data directly from firebase: # fetch raw tag data $ curl -fsSL https://experiments-573d7.firebaseio.com/results/.json > tags.json $ du -hs tags.json 14M tags.json # fetch tag metadata (colors, remapping label names, possibly other stuff in the future) $ curl -fsSL https://experiments-573d7.firebaseio.com/user_meta/.json > tags_…

Can you set it up so that it's only available via cloud? I'm sure that would bother people, but is a better alternative to losing access or you going broke :)

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#46

Post by canada_dry » Wed, Jun 10, 2020, 6:27 PM UTC

TehCorwiz wrote: post #29

jcims wrote: post #23

Earlier quoted context omitted.

>Facial recognition has very few applications outside of total surveillance. That's not really for you to decide, is it? You're absolutely free to have that opinion of course. >We should not respect those who lend it their time and effort. Also your choice of course. Facial recognition is essentially a light integration of powerful underlying technologies. Should 'we' ostracize those working on machine learning, comp…

You didn't really address the author's point which was that there don't appear to be compelling uses of facial technology beyond mass automated surveillance. I can't think of other uses and I'd be interested if you can come up with some.

Here's one: identifying child soldiers.

[i] https://www.pyimagesearch.com/2020/05/11/an-ethical-applicat...

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#47

Post by newen » Wed, Jun 10, 2020, 6:31 PM UTC

hnarayanan wrote: post #6

What does it take to now use this name?

Yeah, it's pretty unethical. Looks like they just stole the name without any care. There doesn't seem to be any relationship between these guys and the original YOLO group.

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#48

Post by jcims » Wed, Jun 10, 2020, 6:31 PM UTC

Has anyone (beyond maybe self-driving software) tried using object tagging as a way to start introducing physics into a scene? E.g. human and bicycle have same motion vector, increases likelihood that human is riding bicycle. Bicycle and human have size and weight ranges that could be used to plot trajectory. Bicycles riding in a straight line and trees both provide some cues as to the gravity vector in the scene. Etc. etc.

Seems like the camera motion is probably already solved with optical flow/photogrammetry stuff, but you might be able to use that to help scale the scene and start filtering your tagging based on geometric likelihood.

The idea of hierarchical reference frames (outlined a bit by Jeff Hawkins here https://www.youtube.com/watch?v=-EVqrDlAqYo&t=3025 ) seems pretty compelling to me for contextualizing scenes to gain comprehension. Particularly if you build a graph from those reference frames and situate models tuned to the type of object at the root of each each frame (vertex). You could use that to help each model learn, too. So if a bike model projects a 'riding' edge towards the 'person' model, there wouldn't likely be much learning. e.g. [Person]-(rides)->[Bike] would have likely been encountered already.

However if the [Bike] projects the (rides) edge towards the [Capuchin] sitting in the seat, the [Capuchin] model might learn that capuchins can (ride) and furthermore they can (ride) a [Bike].

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#49

Post by eeZah7Ux » Wed, Jun 10, 2020, 6:33 PM UTC

rocauc wrote: post #2

EfficientDet was open sourced March 18 [1], YOLOv4 came out April 23 [2], and now YOLOv5 is out only 48 days later. In our initial look, YOLOv5 is 180% faster, 88% smaller, similarly accurate, and easier to use (native to PyTorch rather thank Darknet) than YOLOv4. [1] https://venturebeat.com/2020/03/18/google-ai-open-sources-ef... [2] https://arxiv.org/abs/2004.10934

> open sourced

This is not a verb.

Re: YOLOv5: State-of-the-art object detection at 140 FPS

#50

Post by sillysaurusx » Wed, Jun 10, 2020, 6:33 PM UTC

knicholes wrote: post #37

sillysaurusx wrote: post #18

We made a site that lets you collaboratively tag a bunch of images, called tagpls.com. For example, users decided to re-tag imagenet for fun: https://twitter.com/theshawwn/status/1262535747975868418 And the tags ended up being hilarious: https://pbs.twimg.com/media/EYXRzDAUwAMjXIG?format=jpg&name=... (I'm particularly fond of https://i.imgur.com/ZMz2yUc.png ) The data is freely available via API: https://www.tagpls.c…

I need a dataset and tags for hair, face, neck, arms, left breast, right breast, nipple, torso. Any tips? I'm training a GAN, but I need to specifically segment the parts, as I don't want nipples in the middle of a face. I don't want to have to manually annotate 1,000 images

At the moment, the only experiments with enough data to be useful are e621-portraits (5,407 tags https://www.tagpls.com/exp?n=e621-portraits) and danbooru-e (344 tags https://www.tagpls.com/exp?n=danbooru2019-e) both of which are NSFW.

Those are also drawings/anime, not photos. We have an /r/pics experiment (SFW, 99 tags https://www.tagpls.com/exp?n=r-pics) and /r/gonewild (NSFW, 57 tags https://www.tagpls.com/exp?n=r-gonewild) but currently I haven't gathered enough urls to be very useful -- it only scrapes about 100 or so images every half hour. So there is a lack of tags right now on human photos. We also have a pps experiment (NSFW, exactly what you think it is, 306 tags https://www.tagpls.com/exp?n=pps) but I assume that's not quite what you were looking for.

If you have an idea for a dataset, you can create a list of image URLs like https://battle.shawwn.com/r/pics.txt and we can add them to the site. You can request an addition by joining our ML discord (https://discordapp.com/invite/x52Xz3y) and posting in the #tagging channel.

Also, if anyone's curious, here's how I'm measuring the tag count:

  $ curl -fsSL https://experiments-573d7.firebaseio.com/results/danbooru2019-e/.json | jq '.' | grep points | wc -l
       344
  $ curl -fsSL https://experiments-573d7.firebaseio.com/results/e621-portraits/.json | jq '.' | grep points | wc -l
      5407
  $ curl -fsSL https://experiments-573d7.firebaseio.com/results/r-gonewild/.json | jq '.' | grep points | wc -l
        57
  $ curl -fsSL https://experiments-573d7.firebaseio.com/results/r-pics/.json | jq '.' | grep points | wc -l
        99
  $ curl -fsSL https://experiments-573d7.firebaseio.com/results/pps/.json | jq '.' | grep points | wc -l
       306