Comments (11)
@schluis thank, I didn't notice it.
I also would like to get it done in one network, just the idea of isolation was after talking with @PasGl who reminded me the speed difference of the objects.
Since we have a decent amount of data and a good starting point, I think we can test this out once we figure out the data matters. If someone else can look into HULJs datasets, it would be great as I won't be able to visit the lab soon to copy one.
from hulk.
@jonathan-hellwig ah now I got it. I also think that network in general is fine.
I was using those two datasets, right now converting the first dataset into tfrecords to run a few experiments.
The naodevils one can also be converted to bounding boxes with a bit of work as far as I can see.
@schluis I can be there around 11:00 or some other time.
from hulk.
Telegram group
https://t.me/+eXH48n2Oo34zNTAy
Helpful tools
View NN strucutre: Netron
Draw network structures: diagrams.net (draw.io)
Overview
https://writemd.rz.tuhh.de/Hka2oYKATWym374c8fxcpQ?both
from hulk.
@schluis could we have the architecture diagram or the presentation here? Probably would make it easy to keep an eye :)
A few ideas/ themes I gathered after talking to other hulks;
- We may not want to modify the existing ball network - any damage to quality of detection is unacceptable.
- Alternatively we can have a parallel network like ballet for robot parts, etc without touching the ball net.
- It might be easier to have a slow cycler to run a heavier network for now to detect other stuff -> Balls move fast, others not so fast!
- It might benefit us at a later stage to consider reusing the backbone of ballnet for other parts like positioner. This isn't urgent but probably would save some runtime as the backbone seems to be the heaviest part of the network in terms of runtime.
- Investigate if we can do some autoencoder style training for training/ developing new backbones, etc -> if we can play this right, we can get away with not so many labels or self supervised if it works ..
from hulk.
@tuxbotix I added the image in the linked PR
Regarding 1, I completely agree, but I would like to try to achieve this in one network. If it is measurably worse, I agree that we need to split the networks.
from hulk.
@schmidma and I tried a different approach yesterday. B-Human uses a neural network that outputs bounding boxes of all robots given a grayscale image of 80 x 60. We ran the network on the NAO yesterday and it seems to be working pretty well. We tested it with two robots on the field and it was able to detect robots in almost all poses. We also did some rough benchmarking today. The complete robot detection roughly takes 5 ms - 10 ms to execute.
You can find the preliminary branch at @schmidma's remote.
from hulk.
I think we need to make some adjustments to our current architecture, if we want to integrate the B-Human network into out code base. I suggest we discuss this in a meeting in the next days.
from hulk.
Adjustments due to the runtime or?
I think in that case a new cycler as I mentioned above could.
And if a decision is made to use the BHumans network and not to go forward with further development on this topic, please let me know so I can look into another topic.
from hulk.
I suggest we integrate the B-Human net into our code base such that other teams can proceed with their work. However, I would like to continue to work on robot detection.
I have checked out the data we have available on the BigHulk. We have two categories of data available: classification and object detection. The classification data consists of cropped robot and ball images. The object detection data consists of unlabeled images from past games and images of the GermanOpen 2018 with bounding boxes for balls and robots. I have also checked out some of the datasets available online:
- bounding boxes Maynooth University,
- data for semantic segmentation: B-Human, Nao Devils.
from hulk.
@tuxbotix I was referencing the architecture @schluis posted above. I think the runtime speed using the B-Human network is fine.
from hulk.
@jonathan-hellwig @tuxbotix @alexschirr do you have time on saturday?
from hulk.
Related Issues (20)
- Walk Adjustment
- ChestboardManager: Control REQ_RD_REPLAY failed!!!
- Add HeadInterpolator
- Walk In Hypothesis HOT 2
- Pin `SDK_VERSION` for the CI image action
- Evaluate publishing a container every CI run
- Adapt Visual Referee to Setup Game State
- Partial Visual Referee with sacrificial NAO
- Visual Referee keypoints confidence filtering
- Visual Referee pose height filtering
- First build takes ages because of `openvino` git dependency
- Remove the `24`s
- Indirect Kick Rule
- Forget about balls modes
- Ruleballs in Heatmap
- Kicks in ballfilter
- Evaluate confidence output of neural network
- Penalty Shootout Keeper HOT 5
- Check ideas for Line Detection
- Deny False Positives in Visual Referee Detection
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 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. 📊📈🎉
-
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 ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from hulk.