Comments (10)
No way professionals like Ultralytics claiming 'new standards in SOTA real-time detection and segmentation' without proper comparison on different resolutions, datasets, batch-sizes, GPU-archs.
Surely Ultralytics got better SOTA comparisons then two .png files of accuracy plots vs other YOLO models.
Here some reference that you brought up by yourself in plots from #230 : https://arxiv.org/pdf/2207.02696.pdf
YOLOv7 <...> has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100
Whats YOLOv8 highest AP on COCO val with 30 FPS or higher (on A100 GPU as you did in #230)?
from ultralytics.
@bakeryproducts you can go through this medium blog for understanding of the SOTA comparison and results
[medium blog] (https://kandiraju31.medium.com/sota-metrics-comparison-c1c65755f346)
from ultralytics.
@bakeryproducts @HarishGuragol we'll release comparison plots soon with exact commands to reproduce. There are multiple tasks so it takes comparatively more time. Please be patient.
from ultralytics.
@bakeryproducts SOTA comparisons added in #260
from ultralytics.
@bakeryproducts yes, that's the standard to compare mAP vs speed on coco test set. Every repository does that and thats the main plot. We've provided size comparison too. And more plots are coming in the future. And all these plots will be available in the paper too. FPS too can be calculated using the latency. We need to prioritize bug reports too.
Regarding 56.8 map, those are p6 models. That's a different family of models. Base models (trainer at 640 pixel) and larger p6 models( trained at 1280 pixels). 53.9 is the highest-ever map achieved by the base model still being faster and smaller than the next model in the line.
We cannot compare the different classes of models with very different sizes. v6 had 5 base models and v7 has 2. Both are included in our charts. Our p6 models will be available soon if you're interested in that. We will keep following the release on a rolling basis. hope I answered your questions. If not, please check back in a few days.
from ultralytics.
@glenn-jocher totally agree with @bakeryproducts on that. According to the numbers you're still beyond the Yolo v7 from the paper above. Comparing only to your own results and calling it a "SOTA" is a strong exaggeration
from ultralytics.
So right now highest AP is 53.9 (2.9p behind real-time YOLOv7 56.8) and you are already willing to call that 'setting new standards in SOTA'? Are there any results at higher resolution for v8?
from ultralytics.
@bakeryproducts hi actually the highest model we got is 57.0 with resolution 1280. But we noticed that the settings we used for the model is suboptimal after the training finished, so the 57.0 is suboptimal. We think it's not ready to release if it's suboptimal. And we don't have enough time to train another before release the code. Thanks for the attention. :) We'll release the highest model soon.
from ultralytics.
has there been a followup here @bakeryproducts ?
from ultralytics.
Hey @wesboyt, no, but when the Ultralytics team says "soon," it could take years. No need to worry though! It's been only a year since SOTA claims, and we've been waiting for YOLO papers from them for about three years i think. fingers crossed!
from ultralytics.
Related Issues (20)
- Question regarding mobile app and object detection HOT 1
- Why are width and height values in inferencing output switched? HOT 2
- Understanding the CUDNN_STATUS_NOT_SUPPORTED warning HOT 2
- Conversion from torch model to Onnx model causing variations in inferencing input parameters HOT 2
- How can I optimize my streaming? HOT 3
- Model Detection for similar object HOT 3
- Getting weird error while trying to run ultralytics can someone please help? HOT 2
- How can I use LoadStreams + object detection optimally HOT 10
- When export yolov8 .pt model to onnx ,the output is diff HOT 2
- Selective Cropping from Detected Objects HOT 2
- onnx predict error! HOT 1
- Masks have slight offset HOT 3
- About segmentation HOT 4
- can't find the dataset in python HOT 11
- yoloV8 has lower inference accuracy than yoloV5 HOT 8
- Why set the coordinates of keypoints with confidence less than 0.5 to 0? HOT 2
- Different results on object detection when inputting original img vs pre-resized img(matched to model's trained img size) HOT 2
- Yolov8 does not support albumentations cropping HOT 10
- Output Video stream lag and frame delay HOT 1
- Why did the losses suddenly drop at the endοΌ HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. πππ
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google β€οΈ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ultralytics.