Using transfer learning
[https://blog.roboflow.com/a-primer-on-transfer-learning/] to initialize your
computer vision model from pre-trained weights rather than starting from scratch
(initializing randomly) has been shown to increase performance
When creating a platform on which people can create and share content, there’s
often a question of content moderation
[https://besedo.com/resources/blog/what-is-content-moderation/]. Content
moderation can mean
Machine learning – the software discipline of mapping inputs to outputs without
explicitly programmed relationships – requires substantial computational
resources. Traditionally, this limits where machine learning models can run to
very powerful
You may have heard about OpenAI's CLIP model [https://openai.com/blog/clip/]. If
you looked it up, you read that CLIP stands for "Contrastive Language-Image
Pre-training.
Excitement is building in the artificial intelligence community around MIT's recent release of liquid neural networks. The breakthroughs that Hasani and team have made are incredible. In this post, we will discuss the new liquid neural networks and what they might mean for the vision field.
Can we use object detection to automate identifying moving objects on a screen? Abhinav Mandava leverages Roboflow to create an aimbot (which automates aiming and firing for the player) for Duck Hunt.
Transfer learning [https://blog.roboflow.com/what-is-transfer-learning/] is a
machine learning (ML) technique where knowledge gained during training a set of
problems can be used to solve other related problems.
If you're searching for a dataset to use or are looking to improve your data
science modeling skills, Kaggle [https://www.kaggle.com/] is a great resource
for
Object detection technology advances with the release of Scaled-YOLOv4. This blog is written to help you apply Scaled-YOLOv4 to your custom object detection task, to detect any object in the world, given the right training data.
Suppose you're trying to teach an alien – like one of the crewmates from the
wildly popular game Among Us [http://www.innersloth.com/gameAmongUs.php] – to
tell the
(based on Microsoft COCO benchmarks)
The object detection space remains white hot with the recent publication of
Scaled-YOLOv4 [https://arxiv.org/abs/2011.08036], establishing a new state of
the
The YOLO v4 repository is currently one of the best places to train a custom object detector, and the capabilities of the Darknet repository are vast. In this post, we discuss and implement ten advanced tactics in YOLO v4 so you can build the best object detection model from your custom dataset.