DarkMark
Marking up images for use with Darknet. (by stephanecharette)
DarkHelp
C++ wrapper library for Darknet (by stephanecharette)
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| DarkMark | DarkHelp | |
|---|---|---|
| 8 | 7 | |
| 144 | 130 | |
| - | - | |
| 6.9 | 8.5 | |
| about 2 months ago | 3 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
DarkMark
Posts with mentions or reviews of DarkMark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-13.
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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
DarkHelp
Posts with mentions or reviews of DarkHelp.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-01.
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Tips for making an Object Tracking Dataset
The code is open-source. The project starts with this: https://github.com/stephanecharette/DarkHelp/blob/master/src-apps/video_object_counter.cpp
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Yolov7 with c++
In my repo (https://github.com/stephanecharette/DarkHelp) I say that? Please show me where.
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Trim YOLO / darknet video?
From your description: I'd use the MoveDetect library to find where movement occurs, and then feed just those frames or videos into DarkHelp. Note that DarkHelp also has much richer JSON output than the vanilla Darknet CLI.
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Why is c++ compiling so damn hard?
Came here to say the same. I work mostly on embedded devices, OpenCV, ML/CV type of products. And all our cmake files use globbing, and we love it. Here is an example CMake file for an open source tool I publish on github: https://github.com/stephanecharette/DarkHelp/blob/master/src-lib/CMakeLists.txt
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Object detection CV models for CPU
I use the darknet C++ API, DarkHelp. But to be fair and in the spirit of full disclosure...I wrote it. :)
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Looking for “Computer vision, darknet yolo expert to run code on multi-GPU server”
(I'm the author of DarkHelp, the C++ layer for Darknet.)
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Annotating and detecting objects in a video
DarkHelp will load videos, run inference, and create a new output video. https://github.com/stephanecharette/DarkHelp
What are some alternatives?
When comparing DarkMark and DarkHelp you can also consider the following projects:
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
DarkPlate - License plate parsing using Darknet and YOLO
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
pydensecrf - Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.
django-labeller - An image labelling tool for creating segmentation data sets, for Django and Flask.
VIAME - Video and Image Analytics for Multiple Environments
darknet_ros - YOLO ROS: Real-Time Object Detection for ROS
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
MoveDetect - Library to detect whether movement can be detected between two images or video frames.
evoke - Magic build tool