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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

#101

Post by joshvm »

jcims wrote: post #96
joshvm wrote: post #93

Earlier quoted context omitted.

RGB-D based semantic segmentation is certainly a thing. I'm sure it's also been done with video sequences as well.

Yeah I wish the flagship phone manufacturers would put the hardware back into the phone to take 3d photos...even better if you can get point cloud data to go with it. The applications right now are kind of cheesy but they will get better and if the majority of photos taken pivot to including depth information i think it could really drive better capabilities from our phones. Eyes are very hard to make and coordinate,…

In theory you could also do this with visual-inertial odometry eg monocular SLAM. But this is definitely something we're looking at in my group (I do CV for ecology), especially for object detection where geometry (absolute size) is a good way to distinguish between two confusing classes. A good candidate here is aerial imagery. If you've calibrated the camera and you know your altitude, then you know your ground sample distance (m/px).

Most flagships can do this though, any multicamera phone can get some kind of stereo. Google do it with the PDAF pixels for smart bokeh (they have some nice blog posts about it). I don't know if there is a way to so that in an API though (or to obtain the depth map).

https://ai.googleblog.com/2018/11/learning-to-predict-depth-...

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

#102

Post by joshvm »

whoevercares wrote: post #95
rcpt wrote: post #67

Earlier quoted context omitted.

I love that the response to them is "you can you up,no can no bb" Learned a new phrase today.

你行你上啊 不行别bb（bb=trashtalking/non-favorable comments） This is literally trash talking Slang in Chinese, because this field is full of young bloated researchers who forget their last name

> who forget their last name

I've not heard that one before either. Is it a reference to the Dark Tower? ("[he] has forgotten the face of his father") or did Stephen King borrow it from somewhere else?

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

#103

Post by heyitsguay »

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…

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 application sectors have you found success with?

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

#104

Post by jcims »

joshvm wrote: post #101
jcims wrote: post #96

Earlier quoted context omitted.

Yeah I wish the flagship phone manufacturers would put the hardware back into the phone to take 3d photos...even better if you can get point cloud data to go with it. The applications right now are kind of cheesy but they will get better and if the majority of photos taken pivot to including depth information i think it could really drive better capabilities from our phones. Eyes are very hard to make and coordinate,…

In theory you could also do this with visual-inertial odometry eg monocular SLAM. But this is definitely something we're looking at in my group (I do CV for ecology), especially for object detection where geometry (absolute size) is a good way to distinguish between two confusing classes. A good candidate here is aerial imagery. If you've calibrated the camera and you know your altitude, then you know your ground sam…

High resolution light field cameras would really help here as well. That seems a ways off though.

Are you folks able to do any multi-spectral stuff? That seems interesting.

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

#105

Post by rocauc »

heyitsguay wrote: post #103
rocauc wrote: post #85

Earlier quoted context omitted.

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…

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 at roboflow.ai

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

#106

Post by joshvm »

jcims wrote: post #104
joshvm wrote: post #101

Earlier quoted context omitted.

In theory you could also do this with visual-inertial odometry eg monocular SLAM. But this is definitely something we're looking at in my group (I do CV for ecology), especially for object detection where geometry (absolute size) is a good way to distinguish between two confusing classes. A good candidate here is aerial imagery. If you've calibrated the camera and you know your altitude, then you know your ground sam…

High resolution light field cameras would really help here as well. That seems a ways off though. Are you folks able to do any multi-spectral stuff? That seems interesting.

I work mostly with RGB/Thermal, if that counts. My PhD was in stereo/lidar fusion, so I've always been into mixing sensors :)

I've also done some work on satellite imaging which is 13-band (Sentinel 2). Lots of people in ecology use the Parrot Sequoia which is four-band multispectral. There really isn't much published work in ML beyond RGB, which I find interesting - yes there's RGB-D and LIDAR but it's mostly for driving applications. Part of the reason I'm so familiar with the yolo codebases is that I've had to modify them a lot to work with non-standard data. There's nothing that stops you from using n-channel images, but you will almost certainly have to hack every off the shelf solution to make it work. RGB and 8-bit is almost always hard coded, augmentation also often fails with non RGB data (albumentations is good though). A bigger issue is there's a massive lack of good labelled datasets for non rgb imagery.

On the plus side, in a landscape where everyone is fighting over COCO, there is still a lot of low hanging fruit to pick I think.

I've not done any hyperspectral, very hard to (a) get labelled data (there's AVIRIS and EO-1/Hyperion maybe) (b) it's very hard to label, the images are enormous and (c) the cameras are stupid expensive.

By the way, even satellite imaging ML applications tend to overwhelmingly use just the RGB channels and not the full extent of the data.

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

#107

Post by whoevercares »

joshvm wrote: post #102
whoevercares wrote: post #95

Earlier quoted context omitted.

你行你上啊 不行别bb（bb=trashtalking/non-favorable comments） This is literally trash talking Slang in Chinese, because this field is full of young bloated researchers who forget their last name

> who forget their last name I've not heard that one before either. Is it a reference to the Dark Tower? ("[he] has forgotten the face of his father") or did Stephen King borrow it from somewhere else?

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.

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

#108

Post by nerderloo »

kbenson wrote: post #86
bArray wrote: post #80

Earlier quoted context omitted.

> 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…

AlexeyAB seems to have gotten endorsement from pjreddie: https://github.com/AlexeyAB/darknet/issues/5920#issuecomment...

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

#109

Post by nerderloo »

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…

How does this have anything to do with the linked article?

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

#110

Post by joshvm »

whoevercares wrote: post #107
joshvm wrote: post #102

Earlier quoted context omitted.

> who forget their last name I've not heard that one before either. Is it a reference to the Dark Tower? ("[he] has forgotten the face of his father") or did Stephen King borrow it from somewhere else?

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-...

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