DarkMark
cpp_nn_in_a_weekend
| DarkMark | cpp_nn_in_a_weekend | |
|---|---|---|
| 8 | 1 | |
| 150 | 145 | |
| - | - | |
| 6.9 | 0.0 | |
| 2 months ago | 11 months ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | - |
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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
cpp_nn_in_a_weekend
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C++ Neural Network in a Weekend
The code for the repository is here. I also wrote a paper tutorial walking through the math (the derivations) and the implementation hosted in the same repository here. The paper is... a bit on the long side at 42 pages, but it's meant to be entirely self-contained. Again, from scratch!
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
django-labeller - An image labelling tool for creating segmentation data sets, for Django and Flask.
flashlight - A C++ standalone library for machine learning
VIAME - Video and Image Analytics for Multiple Environments
NerOne - Low level C++ neural network engine. The engine provides a huge flexibility in creating neural networks. It also gives an ability for performance optimisations.
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
layerx-community - LayerX-AI is a comprehensive platform to annotate and manage your machine learning data.