darknet_ros
DarkMark
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| darknet_ros | DarkMark | |
|---|---|---|
| 4 | 8 | |
| 2,076 | 144 | |
| 2.8% | - | |
| 0.0 | 6.9 | |
| 8 months ago | about 2 months ago | |
| C++ | C++ | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
darknet_ros
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Resources for obstacle avoidance
Sorry got swamped with exams, here is the darknet-ros repo: https://github.com/leggedrobotics/darknet_ros
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Yolo in ROS
You are using „darknet_ros“ package. That information probably belongs in the post. I do not have experience with that package. You should look into your logfile and try debug why it is shutting down. If you have further problems with the package. You should try to open a issue on GitHub. https://github.com/leggedrobotics/darknet_ros
Look at Issue here
- How can I Integrate YOLO algorithm in ROS for Lane and Vehicle Detection ?
DarkMark
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Using YOLO for annotation in CVAT
Also see DarkMark. For several years it has had support for loading custom Darknet/YOLO weights (not just MSCOCO!) to help annotate more images. https://www.ccoderun.ca/darkmark/Summary.html
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[Discussion] YOLOv5 training questions, specificaly re-training best practices
You should look at DarkMark. I wrote it specifically to do what you describe. It is an annotation tool that loads the Darknet/YOLO weights, so it can assist in annotating images. I annotate a few images and train, reload DarkMark to annotate some more, train, rinse, lather, repeat.
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When to use YOLOv5 and when not to use the model?
Disclaimer: I'm the author of DarkHelp (the C++ library for Darknet) and DarkMark (the annotation and project management tool for Darknet).
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Annotate data for tracking
If using Darknet/YOLO, look up DarkMark which does have support for video, as well as loading existing neural networks to help annotate images (or video frames) faster. Some info on getting started: https://www.ccoderun.ca/programming/darknet\_faq/#how\_to\_get\_started
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Free AI assisted image labelling tool
You can find DarkMark here: https://github.com/stephanecharette/DarkMark
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Reduce false positive in object detection
Disclaimer: I'm the author of DarkHelp and DarkMark, and I run the Darknet/YOLO discord.
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Extracting Images from Video
I use DarkMark's video import functionality to extract video frames. See this screenshot: https://www.ccoderun.ca/darkmark/Summary.html#DarkMarkImportVideoFrames
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Annotating and detecting objects in a video
DarkMark will extract frames from a video (lots of options, either all frames, sequences of frames, random number of frames, png vs jpeg, resize frames, ...) and then will let you annotate them as you normally would. https://github.com/stephanecharette/DarkMark
What are some alternatives?
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
django-labeller - An image labelling tool for creating segmentation data sets, for Django and Flask.
carla - Open-source simulator for autonomous driving research.
VIAME - Video and Image Analytics for Multiple Environments
DarkHelp - C++ wrapper library for Darknet
DarkPlate - License plate parsing using Darknet and YOLO
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation