With this integration, you can now instantiate SAHI models from Layer. For example, you can pass the path of a YOLOv5 model trained on Layer to instantiate a DetectionModel. Here we fetch the yolo5vs pretrained model from this Layer project.
We have added support for summary stats for your dataset columns on the Layer web interface. We have added percentiles on the data summary statistics to make it easier for you to see a quick description of your datasets. It's now much easier to get a picture of your training data profile.
Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution. Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter.
Layer is the collaboration-first metadata store for production ML that enables build, train and track all of your machine learning project metadata including ML models and datasets with semantic versioning, extensive artifact logging and dynamic reporting with local↔cloud training.