RT-DETR, short for "Real-Time DEtection TRansformer", is a computer vision model developed by Peking University and Baidu. In their paper, "DETRs Beat YOLOs on Real-time Object Detection&
In this blog post, we delve into the process of estimating vehicle speed using computer vision, covering the steps from object detection to tracking and addressing challenges like perspective distortion with OpenCV.
Guide on deploying a CogVLM Inference Server with 4-bit quantization on Amazon Web Services, covering setup of EC2 instances, configuring hardware and software requirements, and starting the inference server with Docker.
Explore the world of image embeddings in computer vision, as we dive into clustering, dataset assessment, and detecting image duplication. Discover dimensionality reduction techniques like t-SNE and UMAP. Use CLIP embeddings for analyzing image class distribution and identifying similar images.
In this comprehensive tutorial, discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model. Learn how to convert object detection datasets into instance segmentation datasets, and use these models to automatically annotate your images.
Most object detection models are trained to identify a narrow predetermined collection of classes. Zero-shot detectors like Grounding DINO want to break this status quo by making it possible to detect new objects without re-training a model.
Learn how Supervision, a new Python package with utilities for building computer vision apps, can help you work through your computer vision projects faster than ever.
Double Detection in Computer Vision
If you’ve been working with object detection long enough, you’ve undoubtedly encountered the problem of double detection. For some reason, the model detects
