Machine learning algorithms are exceptionally data-hungry, requiring thousands –
if not millions – of examples to make informed decisions. Providing high quality
training data for our algorithms to learn is an expensive
In Bedford–Stuyvesant, Brooklyn
Yuri Fukuda regularly walks by a mural that showcases prominent female leaders.
Since October 2005,
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.
When evaluating an object detection model in computer vision, mean average
precision [https://blog.roboflow.com/mean-average-precision/] is the most
commonly cited metric for assessing performance. Remember, mean average
Detectron2 is Facebook's open source library for implementing state-of-the-art
computer vision techniques in PyTorch. Facebook introduced Detectron2
in October 2019 as a complete rewrite
We are pretty excited about the Luxonis OpenCV AI Kit (OAK-D) device at Roboflow, and we're not alone. Our excitement has naturally led us to create another tutorial on how to train and deploy a custom object detection model leveraging Roboflow and DepthAI, to the edge, with depth, faster.
Google Colab [https://colab.research.google.com/] is Google's hosted Jupyter
Notebook product that provides a free compute environment, including GPU and TPU
Computer Vision (and Machine Learning in general) is one of those fields that can seem hard to approach because there are so many industry-specific words (or common words used in novel ways) that it can feel a bit like you're trying to learn a new language when you're trying to get started.
Recently, Roboflow machine learning engineer Jacob Solawetz
[http://jacobsolawetz.com] sat down with Elisha Odemakinde, an ML researcher and
Community Manager at Data Science Nigeria, for a Fireside chat.
At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model.