With this workflow, Nucleus allows you to run inference with standard computer vision models in order to generate a set of baseline predictions. Generating such predictions can be useful when wanting to benchmark your model performance against a standard benchmark. Alternatively, you can use these model predictions to help intelligently sample data before sending it to be annotated.
The standard models currently supported are:
- MSCOCO-trained object detection using the EfficientDet D7 architecture
- MSCOCO-trained panoptic segmentation using the detector2 architecture
- Go to the Models page by clicking on "Models" in the left sidebar menu.
- Scroll to the section titled "Public Models" and click on the desired model (in the video below, we choose object detection).
- Once on the model detail page, click the "Run Model" button in the top right hand corner.
- In the menu, select the dataset name, and optionally slice name over which you want to run model inference.
- After submitting the inference job, you can track the progress in the Job Progress Hub
- When the inference job is finished, you'll be able to visualize the predictions in the nucleus grid!
Updated over 1 year ago