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Repo Claims To Be YOLOv5 #5920
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Read more about YOLOv5:
Comparison YOLOv3 vs YOLOv4 vs YOLOv5: WongKinYiu/CrossStagePartialNetworks#32 (comment)
YOLOv4 achieves 133 - 384 FPS with batch=4 using OpenCV and at least 2x more with batch=32:
Data from:
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@josephofiowa I've updated my comment to reflect you're not the author - sorry. I am just trying to get to the bottom of these dubious claims. |
I'm still confused cuz i thought YOLOv3 was the final one due to ethical concerns. |
It's the last project by pjreddie, but not the last word on YOLO or Darknet. |
Tables 8-10: https://arxiv.org/pdf/2004.10934.pdf
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@glenn-jocher did a lot for the development and improvements of Yolo and showed a lot of ideas, he created at least 2 very good repositories on Pytorch. Thus, he gave Yolo a long life outside of Darknet. All this hype around the Yolov5 was not raised by him. |
Some notes on comparison: https://github.com/ultralytics/yolov5
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Invalid comparison results in the roboflow.ai blog: https://blog.roboflow.ai/yolov5-is-here/ Actually if both networks YOLOv4s and ultralytics-YOLOv5l are trained and tested on the same framework with the same batch on a commond dataset Microsoft COCO: WongKinYiu/CrossStagePartialNetworks#32 (comment)
Full true comparsion: WongKinYiu/CrossStagePartialNetworks#32 (comment)
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@AlexeyAB Thank you for breaking that down. I think my suspicion of the comparisons was warranted. I just noticed that their iOS app page calls their network YOLOv4: https://apps.apple.com/app/id1452689527
Someone said that they were apparently very surprised when you released YOLOv4 as they were planning to also release YOLOv4. I think this really puts emphasis on the need for people to communicate their intentions. |
Yeah. I see it's from Ultralytics LLC, who now becomes of the creator of YOLOv5. I agree your opinion. IMO Ultralytics has intended to succeed to YOLO by implementing PyTorch version with several contributions. Anyway it is the encouraging news for PyTorch community even it doesn't have a significant superior to YOLOv4 of @AlexeyAB. |
I think there is a strong case for either project to adjust their name to reflect the works are not built upon one another and are not a fair comparison. As YOLO started in the Darknet framework, this repository was somewhat endorsed by pjreddie, @AlexeyAB was first to the punch with YOLOv4, Ultralytics already had their own "flavour" of YOLOv3 for TF - it would make sense to rename YOLOv5. Even something small like "uYOLOv5", or "YOuLOv5" could be significant in distinguishing the works. Otherwise who publishes YOLOv6, and is YOLOv6 the improvement from YOLOv4 or YOLOv5? I think this is incredibly confusing and serves nobody. |
It's Joseph, author of that Roboflow blog post announcing Glenn Jocher's YOLOv5 implementation. Our goal is to make models more accessible for anyone to use on their own datasets. Our evaluation on a sample task (BCCD) is meant to highlight tradeoffs and expose differences if one were to clone each repo and use them with little customization. Our post is not intended to be a replacement nor representative of a formal benchmark on COCO. Sincere thanks to the community on your feedback and continued evaluation. We have published a comprehensive updated post on Glenn Jocher's decision to name the model YOLOv5 as well as exactly how to reproduce the results we reported.
@AlexeyAB called out very important notes above that we included in this followup post and updated in the original post. Cloning the YOLOv5 repository defaults to YOLOv5s, and the Darknet implementation defaults to "big YOLOv4." In our sample task, both these models appear to max out their mAP at 0.91 mAP. YOLOv5s is Ultimately, we encourage trying each on one's own problem, and consider the tradeoffs based on your domain considerations (like ease of setup, complexity of task, model size, inference speed reqs). We published guides in the post to make that deliberately easy. And we will continue to listen on where the community lands on what exact name is best for Glenn Jocher's YOLOv5 implementation. |
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@josephofiowa Thank you for your blog post Responding to the Controversy about YOLOv5: YOLOv4 Versus YOLOv5. But I am a little confused about that you wrote. First, in the last sentence of the section "Comparing YOLOv4 and YOLOv5s Model Storage Size" you wrote like this:
Then what about YOLOv5x? Second, in the fourth sentence of the section "Comparing YOLOV4 and YOLOv5s Inference Time" you wrote like this:
It should be 100ms or 50 FPS for YOLOv5s, I might say. Thank you for your post. |
@rcg12387 Why do you think they should know arithmetic? ) |
@rcg12387
Thanks for your callout of the arithmetic error. It's corrected as is the accompanying graph:
Note: Glenn Jocher provided inference time updates and pushed an update to his repo so that times are reported as end-to-end latencies. We have included his comments in the post and pasted them below:
@AlexeyAB |
@josephofiowa But you still don’t know what is the difference between Inference time and FPS ) |
The latest comparison: ultralytics/yolov5#6 (comment) |
@josephofiowa Thank you for your reply. I have read your updated post.
In order to avoid any confusion you should correct this sentence like this: The largest YOLOv5 is YOLOv5x, and its weights are 367 MB. |
Yes, done. Thanks. |
@AlexeyAB Thanks. Following performance updates on ultralytics/yolov5#6. As it is clear Glenn is going to continue to create performance updates (even in the time since the post went live and now) and eventually publish a paper, we will reference that thread in the post for where to find the most up-to-date performance discussion on the COCO benchmark. |
Just to throw a spanner in the works: https://github.com/joe-siyuan-qiao/DetectoRS and https://arxiv.org/pdf/2006.02334.pdf. They claim 73.5 AP50. (I know it has nothing to do with yolo and naming continuity) |
@pfeatherstone
So this is offtopic. |
@pfeatherstone Please don't make a hasty conclusion. A merit of YOLO versions is their lightness and speed. Practitioners don't welcome non-realistic latency even though a model has a high precision. It's useless. |
@AlexeyAB I agree it's off topic. But this thread was comparing latency, FPS and accuracy. I thought i might include other non-yolo based models. Maybe that is more suited to a forum. |
Maybe @glenn-jocher should have branded yolov5 differently to avoid controversy. At the end of the day, pick the one that suits your needs best, I.e performance requirements and your custom dataset. |
@pfeatherstone |
Thanks for the update. It feels like there is competition in the YOLO market... |
I just found out about the controversy believing that YOLOv5 was an upgraded version of YOLOv4 |
python pytorch is popular. its a trend to use pytorch to train darknet model. differnet training system always make me confusing, for example efficientdet in tensorflow | pytorch and darknet backward grad in yolo layer |
YOLOv4 training and inference on different frameworks / libraries: Pytorch-implementations:
TensorFlow: https://github.com/hunglc007/tensorflow-yolov4-tflite OpenCV (YOLOv4 built-in OpenCV): https://github.com/opencv/opencv TensorRT: https://github.com/ceccocats/tkDNN Tencent/NCNN: https://github.com/Tencent/ncnn OpenDataCam: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite BMW-InnovationLab - Training with YOLOv4 has never been so easy (monitor it in many different ways like TensorBoard or a custom REST API and GUI): |
@AlexeyAB why use darknet to train models rather than pytorch? You’re time must be split between research and maintaining/updating darknet. Not trying to be funny or make a point, just trying to understand the reasoning. Wouldn’t you be more productive if you could just focus on models rather than fixing bugs or creating new layers in darknet ? |
By the way, using darknet is also a great solution as a minimal inference framework on CPU as it can have very minimal dependencies. So I can see reasons from a personal point of view. |
This has arrived https://arxiv.org/pdf/2007.12099v2.pdf. Another flavour of yolo... |
@AlexeyAB thanks. Soz for the duplication |
So! which model is better between Yolo v4 and Yolo v5 recommendation for the general user??? and Young students fight about which model is good, but can't draw conclusions |
perfs are similar as it's use same backbones. The only difference is
one is using darknet, the other pytorch.
Le jeu. 6 mai 2021 à 09:44, LEEGILJUN ***@***.***> a écrit :
…
So! which model is better between Yolo v4 and Yolo v5 recommendation for the general user??? and Young students fight about which model is good, but can't draw conclusions
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it is samely similar also Scaled yolov4? |
The one matching your resolution is the optimal. You can take a bigger one
for better accuracy, but it will be slower.
Le ven. 7 mai 2021 à 01:40, LEEGILJUN ***@***.***> a écrit :
… perfs are similar as it's use same backbones. The only difference is one
is using darknet, the other pytorch. Le jeu. 6 mai 2021 à 09:44, LEEGILJUN
*@*.***> a écrit :
… <#m_-2655519042882257752_>
So! which model is better between Yolo v4 and Yolo v5 recommendation for
the general user??? and Young students fight about which model is good, but
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it is samely similar also Scaled yolov4?
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Frankly, the only reason why I used yolov5 over yolov4 is that its documentation(I'm talking about the PYTORCH IMPLEMENTATION; Darknet doesn't really fit my needs) is way easier to understand. The detailed steps for exporting to Onnx, TensorFlow, etc. are all there as well as CLEAR directions on how to use your own data. I tried using the PyTorch implementation of yolov4 and I struggled to actually start training, due to OpenCV and some random value errors that popped up. The directions are super vague and don't give much detail on which directory to place your data, where to keep labels, images, etc. Yolov5 at least has a nice tutorial blog teaching how to use your own data as well as a template colab(from roboflow) that actually works. I was able to export to Onnx and TensorFlow in like 5-10 minutes, while I couldn't even train the PyTorch implementation, because it was so hard figuring out where to put what. So I think that's really the only inherent advantage that the v5 has that makes it worth using over v4. I totally agree that the naming for it is really misleading, but I just wanted to suggest that more documentation be added to this repo and the PyTorch one. |
Also would like to mention that while the performance for yolov4 is so much better than yolov5, I think the ease(or the actual ability) to develop is a reasonable trade off. I think my comment may be a bit unrelated, but just wanted to mention the difference from a developer(who just wants something to work decently)'s point of view |
I'm going to close out this issue - it's mostly been 'resolved' in terms of understanding exactly what happened and there is not much benefit to continue piling in on the issue. Feel free to open a new ticket if new issues arise. |
I test YOLOv4-tiny and YOLOv5s on raspberry PI4 |
@GiorgioSgl Use OpenCV-dnn or OpenVINO or NCNN to test YOLOv4-tiny on Raspberry Pi4:
https://www.reddit.com/r/MachineLearning/comments/hu7lyt/p_yolov4tiny_speed_1770_fps_tensorrtbatch4/ |
@GiorgioSgl
Comparison of YOLOv5 vs Scaled-YOLOv4 / YOLOR: #7717 |
I have tried on TFLite framework and I get better results on FPS, but the performance of the mAP@.5 decrease to a ~27. |
Detection on thermal Infrared Images. Several versions of YOLOv4 and YOLOv5 compared. Details in Table 2. https://www.sciencedirect.com/science/article/pii/S1569843222001145 |
Hey there,
This repo is claiming to be YOLOv5: https://github.com/ultralytics/yolov5
They releaseda blog here: https://blog.roboflow.ai/yolov5-is-here/It's being discussed on HN here: https://news.ycombinator.com/item?id=23478151
In all honesty this looks like some bullshit company stole the name, but it would be good to get some proper word on this @AlexeyAB
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