![]() It has proven to reduce the training time and improve the performance. The model we’ll be using is pretrained on the COCO dataset.įirst, we have to define the complete configuration of the object detection model. These models have been trained on different datasets, and are ready to be used.Įven when people are training their custom dataset, they use these pre-trained weights to initialize their model. Many pre-trained models of Detectron2 can be accessed at model zoo. Import matplotlib.pyplot as plt Using Pretrained model for Inference: Code Let’s also import the common libraries we shall need. import detectron2įrom import setup_loggerįrom detectron2.engine import DefaultPredictorįrom import Visualizerįrom detectron2.data import MetadataCatalog, DatasetCatalogįrom detectron2.structures import BoxMode ![]() Now, you have to import detectron2 and its modules. The first step is to install the detectron2 library and the required dependencies import torch I’ll cover an example in the next section. It provides pre-trained models which you can easily load and use it on new images. It supports multiple tasks such as bounding box detection, instance segmentation, keypoint detection, densepose detection, and so on. It requires CUDA due to the heavy computations involved. Detectron2 is based upon the maskrcnn benchmark. Introducing Detectron2įacebook AI Research (FAIR) came up with this advanced library, which gave amazing results on object detection and segmentation problems. ![]() In this article, I’ll perform object detection using a recent, robust model called Detectron2. This is one example that involves object detection. You can observe that the app is able to identify objects from pictures and use them to classify them into broader categories.
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