Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Plug and Play Real-Time Object Detection App with Tensorflow and OpenCV
| Date | Stars |
|---|---|
| 2026-07-24 | 285 |
| 2026-07-25 | 285 |
| 2026-07-28 | 285 |
| 2026-07-30 | 285 |
| 2026-08-06 | 285 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# realtime_object_detection
Realtime Object Detection based on Tensorflow's [Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) and [DeepLab Project](https://github.com/tensorflow/models/tree/master/research/deeplab)
<img src="test_images/rod.png" width="75.0%">
> Version 1: use branch [r1.0](https://github.com/GustavZ/realtime_object_detection/tree/r1.0) for the original repo that was focused on high performance inference of `ssd_mobilenet` <br />
(*x10 Performance Increase on Nvidia Jetson TX2*)
> Version 2: use branch [Master](https://github.com/GustavZ/realtime_object_detection/tree/master) or to be additionally able to run and test Mask-Detection Models, KCF-Tracking and DeepLab Models (*merge of the repo [realtime_segmenation](https://github.com/GustavZ/realtime_segmenation)*)
> ROS Support: To use this Repo as ROS-Package including detection and segmentation ROS-Nodes use branch [ros](https://github.com/GustavZ/realtime_object_detection/tree/ros).
Alternativley use the repo [objectdetection_ros](https://github.com/GustavZ/objectdetection_ros)
## About the Project
The Idea was to create a scaleable realtime-capable object detection pipeline that runs on various systems. <br />
Plug and play, ready to use without deep previous knowledge.<br /> <br />
The project includes following work:
- optional download of tensorflow pretrained models
- do Inference with OpenCV, either through video input or on selected test_images. <br />
supported Models are all `research/object_detection` as well as `research/deeplab` models
- enjoy this project's own `ssd_mobilenet` speed hack, which splits the model in a mutlithreaded cpu and gpu session. <br />
Results in up to x10 performance increase depending on the running system <br />
⇒ which makes it (one of) the fastest inference piplines out there
- run statistic tests on sets of images and get statistical information like mean and median fps, std dev and much more
- create `timeline` files measuring the exact time consumption of each operation in your model
- inspect, summarize, quantize, transform and benchmark models with the provided `scripts/`
- Use this Repo as ROS Package. the detection subscirbes a ROS Image topic and publishes the detection as ROS Node.
## Inference:
- create a copy of `config.sample.yml` named `config.yml` and only change configurations inside this file <br />
For example: If you are not interested in visualization: set `VISUALIZE` to `False`, <br />
or if you want to switch off the speed hack set `SPLIT_MODEL` to `False`, <br />
- to be able to use KCF_Tracking inside `scripts/` run `bash build_kcf.sh` to build it and set `USE_TRACKER` to `True` to use it <br />
(currently only works for pure object detection models without `SPLIT_MODEL`)
- new class (Model,Config,Visualizer) structure. Simply create your own test file with:
```
from rod.model import ObjectDetectionModel, DeepLabModel
from rod.config import Config
model_type = 'od' #or 'dl'
input_type = 'video' #or 'image'
config = Config(model_type)
model = ObjectDetectionModel(config).prepare_model(input_type) #or DeepLabModel
model.run()
```
- Alternativley run `python` + `objectdetection_video.py` or `objectdetection_image.py` or `deeplab_video.py` or `deeplab_image.py` or `allmodels_image.py`
## Scripts:
To make use of the tools provided inside `scripts/` follow this guide: <br />
- first change all paths and variables inside `config_tools.sh` to your needs / according to your system
- When using **the first time** run: `source config_tools.sh` and in the same terminal **run only once** `source build_tools.sh` to build the tools. this will take a while. <br />
- For all following uses first run: `source config_tools.sh`(due to the exported variables) and after that you are able to run the wanted scripts **always from the same teExcerpt of 6,103 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:88f00cbc451cc50b, topic:object-detection, topic:opencv, name:object detection
matched fp:88f00cbc451cc50b, topic:deep-learning, topic:tensorflow