Towards AI·2yBuilding your own Object Detector from Scratch with Tensorflow
This post discusses building an object detector from scratch using TensorFlow. It covers the model architecture, tasks involved in object detection, and the training process. The post also mentions the importance of data augmentation and improving model capacity to achieve better results.
Towards AI·1yComputer Vision — Object Detection Task
Object detection is an advanced version of object localization, involving identifying multiple objects and drawing bounding boxes around them. There are two types of models: two-stage models, which are outdated, and single-stage models, which are faster and easier to train. To solve the issue of predicting a fixed number of bounding boxes irrespective of actual objects, researchers developed techniques such as the Hungarian Matching Algorithm and various versions of the YOLO model. The post discusses the progression and implementation of these methods.
Hacker News·2yPsyChip/machina: OpenCV+YOLO+LLAVA powered video surveillance system
A high-resolution RTSP stream connects to a system using OpenCV and YOLO. Frames are queued, resampled, and processed to match objects with specific IDs by coordinates, size, and timestamp, making LLM requests to the Ollama server for object tagging. The system operates with a 640x480 resolution for processing and averages 20ms interference time. Instructions include cloning the repository, installing required dependencies, and running the script. Persistent objects can be observed, and the project aims to develop a comprehensive headless security system.
DigitalOcean Community·2yFaster R-CNN Explained for Object Detection Tasks
The post reviews the Faster R-CNN model developed for object detection, emphasizing its evolution from R-CNN and Fast R-CNN. It explains the architecture, including the Region Proposal Network (RPN) that improves speed and accuracy in predicting object locations. Despite some drawbacks, Faster R-CNN is highlighted as a state-of-the-art model for object detection, with Mask R-CNN being an advanced extension that adds object masks.
Towards AI·1yReal-Time Object Detection using YoloV7 on Google Colab
Learn how to perform real-time object detection using YOLOv7 on Google Colab in this detailed tutorial. Understand the structure of the training data and the bounding box representation used in YOLO models, and follow steps to apply the model to your videos.
DigitalOcean Community·2yHow to train and use a custom YOLOv7 model
YOLOv7 is the latest iteration of the YOLO object detection model, offering significant improvements over previous versions due to enhancements like model re-parameterization, E-ELAN techniques, and compound scaling. The tutorial covers the theoretical background, practical steps for training a custom YOLOv7 model, and a detailed coding demo using NBA game footage to identify the ball handler. Key steps include dataset preparation, labeling using RoboFlow, model training, and performance evaluation.
PyImageSearch·2yAutomatic License Plate Reader Using OCR in Python
This post provides a tutorial on how to build an Automatic License Plate/Number Plate Reader (ANPR) system using PaddleOCR, Hugging Face transformers, and Python. The post covers topics such as license plate reader methods, modern-day object detectors, OWL-ViT, PaddleOCR, configuring the development environment, object detection, OCR, and the use of Gradio for creating an interactive web interface.
Community Picks·2yUsing YOLO with CLIP to improve Retrieval
Combining YOLO object detection with CLIP's multimodal embedding improves image retrieval by decomposing images into objects, embedding these objects, and linking them to the parent image. This process enhances retrieval accuracy, especially for images with multiple or background objects. The tutorial includes setting up dependencies, downloading datasets, initiating models, and embedding images for similarity checks.