Identify segments in an image using a Detectron2 model
State-of-the-art Object Detection YOLOV9 Demo
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Detectron2 Model Demo is a state-of-the-art object detection tool that leverages the powerful Detectron2 framework to identify segments and objects within images. This demo provides an interactive way to experience the capabilities of the Detectron2 model, enabling users to visualize detection results on their own images.
pip install detectron2MXNet or follow the official installation guide.input_image.jpg).from detectron2.utils.visualizer import Visualizer
import cv2
img = cv2.imread("input_image.jpg")
outputs = predictor(img)
visualizer = Visualizer(img[:, :, ::-1])
result = visualizer.draw_instance_predictions(outputs["instances"].to(cpu))
cv2.imwrite("output_image.jpg", result.get_image()[:, :, ::-1])
output_image.jpg) to see the detection results overlaid on your input image.What is the primary function of Detectron2 Model Demo?
The primary function is to demonstrate object detection capabilities by analyzing images and highlighting detected objects with bounding boxes and segmentation masks.
What types of objects can Detectron2 Model Demo detect?
Detectron2 Model Demo can detect a wide variety of objects, including people, animals, vehicles, and everyday items, based on the pre-trained models used.
Can I use custom datasets with Detectron2 Model Demo?
Yes, you can fine-tune the model using custom datasets for specific object detection tasks. This requires additional training steps beyond the demo setup.
How do I improve the accuracy of the detection results?
To improve accuracy, you can fine-tune the model with your own dataset, increase the resolution of the input images, or experiment with different pre-trained models provided by Detectron2.