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

blog.roboflow.ai

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

#81

Post by sillysaurusx »

bArray wrote: post #63

I'm just going to call this out as bullshit. This isn't YOLOv5. I doubt they even did a proper comparison between their model and YOLOv4. Someone asked it to not be called YOLOv5 and their response was just awful [1]. They also blew off a request to publish a blog/paper detailing the network [2]. I filed a ticket to get to the bottom of this with the creators of YOLOv4: https://github.com/AlexeyAB/darknet/issues/5920…

There are some benchmarks here: https://github.com/WongKinYiu/CrossStagePartialNetworks/issu...

It's hard to interpret benchmarks in a fair way, but it's sort of sounding like YOLOv4 might be superior to YOLOv5, at least for certain resolutions.

Does YOLOv5 outperform YOLOv4 at all? Faster inference time or higher accuracy?

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

#83

Post by tropshop »

homarp wrote: post #53
homarp wrote: post #51

Earlier quoted context omitted.

Some compelling use: 1) assistance to recognizing people (because low vision, because memory fails, because you have a lot of photos...) 2) ensure candidate X is actually candidate X and not a paid person to take the exam in name of candidate X 3) door access control (to replace/in addition to access card) 4) having your own X-Ray (like in Amazon Prime): identify an actor/actress/model 5) having your personal robot a…

also, having models that can run locally on "cheap enough" devices is also quite interesting that you more or less understand. Compared to using API in the cloud or purchasing Hikvision cameras.

Just getting into this. Do you recommend any particular "dumb" camera devices with a quality stream?

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

#84

Post by hikarudo »

travisporter wrote: post #42
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"

"You only look once" in Chinese.

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

#85

Post by rocauc »

bArray wrote: post #63

I'm just going to call this out as bullshit. This isn't YOLOv5. I doubt they even did a proper comparison between their model and YOLOv4. Someone asked it to not be called YOLOv5 and their response was just awful [1]. They also blew off a request to publish a blog/paper detailing the network [2]. I filed a ticket to get to the bottom of this with the creators of YOLOv4: https://github.com/AlexeyAB/darknet/issues/5920…

Hey all - OP here. We're not affiliated with Ultralytics or the other researchers. We're a startup that enables developers to use computer vision without being machine learning experts, and we support a wide array of open source model architectures for teams to try on their data: https://models.roboflow.ai

Beyond that, we're just fans. We're amazed by how quickly the field is moving and we did some benchmarks that we thought other people might find as exciting as we did. I don't want to take a side in the naming controversy. Our core focus is helping developers get data into any model, regardless of its name!

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

#86

Post by kbenson »

bArray wrote: post #80
kbenson wrote: post #72

Earlier quoted context omitted.

Just found these.[1][2] That is pretty awful, if it's from a dev. Edit: Although as yeldarb explains in a comment here[3], it's probably a bit more complicated than that. 1: https://www.urbandictionary.com/define.php?term=you%20can%20... 2: https://www.quora.com/Whats-the-meaning-of-you-can-you-up-no... 3: https://news.ycombinator.com/item?id=23478983

> Edit: Although as yeldarb explains in a comment here[3], > it's probably a bit more complicated than that. Legally speaking I'm not sure anything wrong was really done here. Morally speaking, it seems quite unethical. AlexeyAB has really been carrying the torch of the Darknet framework and the YOLO neural network for quite some time (with pjreddie effectively handing it over to him). AlexeyAB has been providing sup…

So, the question I have is whether AlexeyAB got some sort of endorsement from pjreddie, or if they just took over the name by nature of being the most active fork? If it's the latter, ultralytics' actions don't seem quite as bad (although they still feel kind of off-putting, especially with how some of the responses to calls for a name change were formulated).

I guess given the info I have now, to me it boils down to whether there's precedent for the next version of the name to be taken by whoever is doing work on it? If the original author never endorsed AlexeyAB (I don't know one way or another), then perhaps AlexeyAB should have changed the name but references or payed homage to YOLO in some way?

Eh, this is all starting to feel a bit too close to youtube drama for my liking.

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

#87

Post by sillysaurusx »

rocauc wrote: post #85
bArray wrote: post #63

I'm just going to call this out as bullshit. This isn't YOLOv5. I doubt they even did a proper comparison between their model and YOLOv4. Someone asked it to not be called YOLOv5 and their response was just awful [1]. They also blew off a request to publish a blog/paper detailing the network [2]. I filed a ticket to get to the bottom of this with the creators of YOLOv4: https://github.com/AlexeyAB/darknet/issues/5920…

Hey all - OP here. We're not affiliated with Ultralytics or the other researchers. We're a startup that enables developers to use computer vision without being machine learning experts, and we support a wide array of open source model architectures for teams to try on their data: https://models.roboflow.ai Beyond that, we're just fans. We're amazed by how quickly the field is moving and we did some benchmarks that we…

YOLOv5 seems to have one important advantage over v4, which your post helped highlight:

Fourth, YOLOv5 is small. Specifically, a weights file for YOLOv5 is 27 megabytes. Our weights file for YOLOv4 (with Darknet architecture) is 244 megabytes. YOLOv5 is nearly 90 percent smaller than YOLOv4. This means YOLOv5 can be deployed to embedded devices much more easily.

Naming controversy aside, it's nice to have some model that can get close to the same accuracy at 10% of the size.

Naming it v5 was certainly ... bold ... though. If it can't outperform v4 in any scenario, is it really worthy of the name? (On the other hand, if v5 can beat v4 in inference time or accuracy, that should be highlighted somewhere.)

FWIW I doubt anyone who looks into this will think roboflow had anything to do with the current controversies. You just showed off what someone else made, which is both legit and helpful. It's not like you were the ones that named it v5.

On the other hand... visiting https://models.roboflow.ai/ does show YOLOv5 as "current SOTA", with some impressive-sounding results:

SIZE: YOLOv5 is about 88% smaller than YOLOv4 (27 MB vs 244 MB)

SPEED: YOLOv5 is about 180% faster than YOLOv4 (140 FPS vs 50 FPS)

ACCURACY: YOLOv5 is roughly as accurate as YOLOv4 on the same task (0.895 mAP vs 0.892 mAP)

Then it links to https://blog.roboflow.ai/yolov5-is-here/ but there doesn't seem to be any clear chart showing "here's v5 performance vs v4 performance under these conditions: x, y, z"

Out of curiosity, where did the "180% faster" and 0.895 mAP vs 0.892 mAP numbers come from? Is there some way to reproduce those measurements?

The benchmarks at https://github.com/WongKinYiu/CrossStagePartialNetworks/issu... seem to show different results, with v4 coming out ahead in both accuracy and speed at 736x736 res. I'm not sure if they're using a standard benchmarking script though.

Thanks for gathering together what's currently known. The field does move fast.

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

#88

Post by knicholes »

sillysaurusx wrote: post #50
knicholes wrote: post #37

Earlier quoted context omitted.

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…

Yeah, I mean, the tagging is awesome, but I'm thinking I'll need more image segmentation than object recognition. With a segmentation map, I can make a great image->image translator.

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

#90

Post by quietbritishjim »

bArray wrote: post #63

I'm just going to call this out as bullshit. This isn't YOLOv5. I doubt they even did a proper comparison between their model and YOLOv4. Someone asked it to not be called YOLOv5 and their response was just awful [1]. They also blew off a request to publish a blog/paper detailing the network [2]. I filed a ticket to get to the bottom of this with the creators of YOLOv4: https://github.com/AlexeyAB/darknet/issues/5920…

> Someone asked it to not be called YOLOv5 and their response was just awful [1]

I don't see any response by them at all. Do you mean the comment by WDNMD0-0? I can't see any reason to believe they're connected to the company, have I missed something?

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