Think Different

  #DataGemma  is an experimental set of #open #models designed to ground responses in #realworld #statistical #data from numerous #public #sources ranging from census and health bureaus to the #UN , resulting in more factual and trustworthy AI. By integrating with Google ’s #Data Commons, DataGemma’s early research advancements attempt to address the issue of #hallucination—a key challenge faced by language models #llm . What is the Data Commons? Google Data Commons: A Knowledge Graph for Public Data Google Data Commons is a public knowledge graph that integrates and harmonizes data from various sources, making it easier to explore and analyze. It's designed to provide a unified view of the world's information, enabling users to discover insights and trends across different domains. Key Features and Benefits: Unified Dataset: Data Commons combines data from over 200 sources, including government statistics, academic research, and private sector data. This creates a ...

  pexel End-to-End Number Plate Detection and Recognition using YOLO Application Flow: Image Capture: Acquire an image of a vehicle. Image Preprocessing: Resize and normalize the image. Number Plate Detection: Use YOLOv3 (or YOLOv4/v5) to locate the number plate region. Number Plate Extraction: Crop the detected region from the original image. Image Enhancement: Improve the quality of the extracted image (e.g., thresholding, edge detection). OCR: Use Tesseract-OCR to recognize text from the enhanced image. Number Plate Recognition: Validate and format the extracted text. Implementation Details: YOLO Model: Use a pre-trained YOLO model and fine-tune it on a dataset of number plate images. OCR Library: Employ Tesseract-OCR with a custom-trained model for number plate fonts. Programming Language: Python is a popular choice, with libraries like OpenCV, NumPy, and PyTesseract. Example Code Snippet (Python): Python import cv2 import numpy as np import pytesserac...