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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

#91

Post by rocauc »

sillysaurusx wrote: post #87
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…

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…

Agreed!

Crucially, we're tracking "out of the box" performance, e.g., if a developer grabbed X model and used it on a sample task, how could they expect it to perform? Further research and evaluation is recommended!

For size, we measured the sizes of our saved weights files for Darknet YOLOv4 versus the PyTorch YOLOv5 implementation.

For inference speed, we checked "out of the box" speed using a Colab Notebook equipped with a Tesla P100. We used the same task[1] for both - e.g. see the YOLOv5 Colab notebook[2]. For Darknet YOLOv4 inference speed, we translated the Darknet weights using the Ultralytics YOLOv3 repo (as we've seen many do for deployments)[3]. (To achieve top YOLOv4 inference speed, one should reconfigure Darknet carefully with OpenCV, CUDA, cuDNN, and carefully monitor batch size.)

For accuracy, we evaluated the task above with mAP after quick training (100 epochs) with the smallest YOLOv5s model against the full YOLOv4 model (using recommended 2000*n, n is classes). Our example is a small custom dataset, and should be investigated on e.g. COCO. 90-classes.

[1] https://public.roboflow.ai/object-detection/bccd [2] https://colab.research.google.com/drive/1gDZ2xcTOgR39tGGs-EZ... [3] https://github.com/ultralytics/yolov3

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

#92

Post by joshvm »

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 implementation which was inference-only. Their claim to fame is that they were the first to get training to work in Pytorch. This took a while, probably because there is actually very little documentation for Yolov3 and there was confusion over what the loss function actually ought to be. The darknet repo is totally uncommented C with lots of single letter variable names. AlexeyAB is a Saint.

That said, should it be a totally new name? The changes are indeed relatively minor in terms of architecture, it's still yolo underneath (in fact I think the classification/regression head is pretty much unchanged). The v4 release was also quite contentious. Actually their previous models used to be called yolov3-spp-ultralytics.

Probably I would have gone with efficient-yolo or something similar. That's no worse than fast/faster rcnn.

I disagree on your second point though. Demanding a paper when the author says "we will later" is hardly a blow off. Publishing and writing takes time. The code is open source, the implementation is there. How many times does it happen the other way around? And before we knock Glenn for this, as far as I know, he's running a business, not a research group.

Disclosure: I've contributed (in minor ways) to both this repository and Alexey's darknet fork. I use both regularly for work and I would say I'm familiar enough with both codebases. I mostly ignore the benchmarks because performance on coco is meaningless for performance on custom data. I'm not affiliated with either group, in case it's not clear.

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

#93

Post by joshvm »

jcims wrote: post #48

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. Et…

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

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

#94

Post by [deleted] »

rocauc wrote: post #91
sillysaurusx wrote: post #87

Earlier quoted context omitted.

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…

Agreed! Crucially, we're tracking "out of the box" performance, e.g., if a developer grabbed X model and used it on a sample task, how could they expect it to perform? Further research and evaluation is recommended! For size, we measured the sizes of our saved weights files for Darknet YOLOv4 versus the PyTorch YOLOv5 implementation. For inference speed, we checked "out of the box" speed using a Colab Notebook equipp…

[deleted]

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

#95

Post by whoevercares »

rcpt wrote: post #67
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 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

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

#96

Post by jcims »

joshvm wrote: post #93
jcims wrote: post #48

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. Et…

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, yet there are almost no cyclops in nature.

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

#97

Post by oehtXRwMkIs »

syntaxing wrote: post #62
nharada wrote: post #58

I welcome forward progress in the field, but something about this doesn't sit right with me. The authors have an unpublished/unreviewed set of results and they're already co-opting the YOLO name (without the original author) for it and all of this to promote a company? I guess this was inevitable when there's so much money in ML but it definitely feels against the spirit of the academic research community that they'r…

Totally agreed, kinda seems dirty to call something "v5" when it this is a derivative work of the original.

I think derivative is a bit generous. This is just a reimplementation of v4 with a different framework.

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

#98

Post by DEDLINE »

fattire wrote: post #82
DEDLINE wrote: post #36

Does anyone know of an open-source equivalent to YOLOv5 in the sound recognition / classification domain? Paid?

Like it would identify what you're hearing? "Trumpet!" "Wind whistling through oak leaves!" "Male child!" etc?

Ubicoustics [1] would be the closest example to what I am looking for in a FOSS / Commercial offering. Is anyone working on this?

[1] https://github.com/FIGLAB/ubicoustics

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

#99

Post by Der_Einzige »

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…

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 .

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

#100

Post by sicariusnoctis »

nharada wrote: post #58

I welcome forward progress in the field, but something about this doesn't sit right with me. The authors have an unpublished/unreviewed set of results and they're already co-opting the YOLO name (without the original author) for it and all of this to promote a company? I guess this was inevitable when there's so much money in ML but it definitely feels against the spirit of the academic research community that they'r…

> there's so much money in ML

What do you mean? I thought the DL hypetrain was dying as companies failed to make returns on their investments.

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