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

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

#111

Post by whoevercares »

joshvm wrote: post #110
whoevercares wrote: post #107

Earlier quoted context omitted.

This is an old punchline in China for many years and I doubt it comes from English literature. I guess the meaning is similar (last name ~= name of the father) Edit: obviously I should google dark power first lol.

Also a slight edit, I wrote name initially. Of course in the books it's "face of his father", but it still sounds similar [1]. To admit to forgetting the face of one's father is to be deeply shameful, to accuse someone of it is insinuating they should be ashamed of themselves. Can you write it in Chinese? [1] https://www.goodreads.com/quotes/12991-i-do-not-aim-with-my-...

“不知道自己姓什么了”

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

#112

Post by nerderloo »

joshvm wrote: post #92
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…

I somewhat agree on the naming issue. I don't think yolov5 is semantically very informative. But by the way, if you read the issues from a while back you'll see that AlexeyAB's fork basically scooped them, hence the version bump. Ultralytics probably would have called this Yolov4 otherwise. This repo has been in the works for a while. For history, Ultralytics originally forked the core code from some other Pytorch im…

Didn't AlexeyAB endorse YOLOv4 though? Did he also endorse YOLOv5?

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

#113

Post by mycall »

rocauc wrote: post #105
heyitsguay wrote: post #103

Earlier quoted context omitted.

Very cool business model! How long have you been at it? I've been pushing for a while (unsuccessfully, so far) for the NIH to cultivate a team providing such a service to our many biomedical imaging labs. It seems pretty clear to me that this sort of AI hub model is going to win out in at least the medium term versus spending money on lots of small redundant AI teams each dedicated to a single project. What sort of a…

Appreciate it! Nice, I really respect research coming out of NIH. (Happen to know Travis Hoppe?) Coincidentally, our notebook demo for YOLOv5 is on the blood cell count and detection dataset: https://public.roboflow.ai/object-detection/bccd We've seen 1000+ different use cases. Some of the most popular are in agriculture (weeds vs crops), industrials / production (quality assurance), and OCR. Send me an email? joseph…

Do you know of any battery-wired drones that can pick out invasive plants? I've been looking for this to use on trails but since the plant's sap is highly poisonous, drones seem to be the logical solution.

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

#114

Post by joshvm »

nerderloo wrote: post #112
joshvm wrote: post #92

Earlier quoted context omitted.

I somewhat agree on the naming issue. I don't think yolov5 is semantically very informative. But by the way, if you read the issues from a while back you'll see that AlexeyAB's fork basically scooped them, hence the version bump. Ultralytics probably would have called this Yolov4 otherwise. This repo has been in the works for a while. For history, Ultralytics originally forked the core code from some other Pytorch im…

Didn't AlexeyAB endorse YOLOv4 though? Did he also endorse YOLOv5?

AlexeyAB is the primary author on YOLOv4, and the darknet maintainer, so yes. This is pretty much the official word on the matter:

https://github.com/AlexeyAB/darknet/issues/5920#issuecomment...

Despite that, there was still a lot of controversy over the decision to call it v4.

See that thread for the discussion on v5 and you can make your own judgement.

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

#115

Post by heisenburgzero »

This is not the first time something is fishy. Back in the early stages of the repo. They were advertising on the front page that they are achieving similar MAP to the original C++ version. But only to be found out they haven't train it on COCO dataset and test it.

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

#116

Post by nerderloo »

joshvm wrote: post #114
nerderloo wrote: post #112

Earlier quoted context omitted.

Didn't AlexeyAB endorse YOLOv4 though? Did he also endorse YOLOv5?

AlexeyAB is the primary author on YOLOv4, and the darknet maintainer, so yes. This is pretty much the official word on the matter: https://github.com/AlexeyAB/darknet/issues/5920#issuecomment... Despite that, there was still a lot of controversy over the decision to call it v4. See that thread for the discussion on v5 and you can make your own judgement.

Ah, I misspoke. I meant prjeddie. prjeddie kind of endorsed YOLOv4. Did he endorse YOLOv5?

Although YOLOv4 isn't anything new achitecture-wise, it tried all the tricks in the book on the existing YOLO architecture to increase its speed performance, and its method and experiment results were published as a paper; it provided value to humanity.

YOLOv5 seemed to have taken the YOLO name to seemingly only to increase the startup name value without giving much(it did appear to provided YOLOv3 Pytorch implementation, but that's before taking YOLOv5 name) back. I wonder how prjeddie would think of YOLOv5.

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

#117

Post by cgarciae »

ely-s wrote: post #27

There seems to be an unfair comparison between the various network architectures. The reported speed and accuracy improvements should be taken with a bit of scepticism for two reasons. * This is the first yolo implemented in Pytorch. Pytorch is the fastest ml framework around, so some of YOLOv5's speed improvements may be attributed to the platform it was implemented on rather than actual scientific advances. Previou…

Side note: I like Pytorch but eager pytorch is not faster the jax.jit or tf.function code

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

#118

Post by jcun4128 »

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

> I don't want nipples in the middle of a face

There is a market somewhere

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

#119

Post by jcun4128 »

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…

This is really cool, thanks for sharing

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

#120

Post by jcun4128 »

Der_Einzige wrote: post #99
sillysaurusx wrote: post #50

Earlier quoted context omitted.

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…

I love that it's porn (and specifically furry/hentai) which pushes the limits of image recognition and creativity within computer vision. Between this and the de-censoring tool "DeepCreamPy" I can't look most data scientists in the face anymore .

that's a great name, turning jagged edges back to smooth and applying reverse Gaussian blur /s

on a serious note, kind of interesting the authenticity/accuracy if it's just filled in... eg. turning black and white pictures back to color eg. was it actually green or blue

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