Joseph Nelson

17 May 2021 • 4 min read

Featuring rock, paper, scissors. OpenAI's CLIP model (Contrastive Language-Image Pre-Training) is a powerful zero-shot classifier that leverages knowledge of the English language to classify images without having to be trained on any specific dataset. In other words, CLIP already knows a lot about the content of images without

20 Mar 2021 • 4 min read

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 supercomputers. But this is changing. Computation is required at two core moments in the machine learning development lifecycle: model training

8 Feb 2021 • 7 min read

In this post, we will leverage Roboflow and the Luxonis OAK to train and deploy a custom license plate model to your OAK device.

13 Jan 2021 • 4 min read

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. It’s very similar to the concept of reusing code in computer programming, except instead of sharing code between different applications or software projects, we’

3 Dec 2020 • 8 min read

(based on Microsoft COCO benchmarks) The object detection space remains white hot with the recent publication of Scaled-YOLOv4, establishing a new state of the art in object detection. ⚡RF-DETR Neural Architecture Search (NAS) is faster and more accurate than Scaled-YOLOv4. Read the blog post here. Looking to train a Scaled-YOLOv4