image-quality-assessment
DarkMark
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| image-quality-assessment | DarkMark | |
|---|---|---|
| 2 | 8 | |
| 1,994 | 143 | |
| 1.6% | - | |
| 0.0 | 6.9 | |
| 4 months ago | about 1 month ago | |
| Python | C++ | |
| Apache License 2.0 | 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.
image-quality-assessment
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And so it begins: AI(Midjourney) wins art competition without anyone realising.
It's an ongoing research area, but here's a Google model that assigns images ratings based on how good they look. It's pretty good, and it's from 2019 so not even close to state of the art. Paired with Stable Diffusion, it could indeed curate itself. I might have to try that actually.
- Extracting Images from Video
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?
student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
django-labeller - An image labelling tool for creating segmentation data sets, for Django and Flask.
imagededup - 😎 Finding duplicate images made easy!
VIAME - Video and Image Analytics for Multiple Environments
These-People-Do-Not-Exist - AI that generates human faces which have never been seen before. The future is now 😁
DarkHelp - C++ wrapper library for Darknet
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
DarkPlate - License plate parsing using Darknet and YOLO