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Source channel @githubtrending · Post #14949 · Jul 12

#jupyter_notebook#darknet#pytorch#scaled_yolov4#yolor#yolov3#yolov4#yolov7 YOLOv7 is a powerful tool for detecting objects in images and videos. It is fast, accurate, and can work well on devices with limited power, making it useful for real-time applications like self-driving cars and surveillance systems. YOLOv7 uses advanced techniques like Feature Pyramid Networks to detect objects of different sizes and can handle complex scenes with overlapping objects. This makes it beneficial for users who need quick and precise object detection in various environments. https://github.com/WongKinYiu/yolov7

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@NEWS_AND_TIPS · Post #2362 · 05/26/2024, 05:46 PM

Telegram #FactCheck Feature Spotted in Code. We spotted some interesting code snippets that hint at a new "Fact Check" being in development. Here's what we found: 1. A dedicated area within the application for users to access fact-checking tools or information. 2. Fact-Check Submission and Editing: Users might be able to submit facts to be checked, edit their submissions, and delete them. 3. Fact-Checking Dialogs: Dialog boxes to interact with the fact-checking process, perhaps to confirm submissions, display detailed information, or edit entries. 4. User Notifications: Notifications and messages to inform users about the status of their fact-checks (submitted, edited, deleted). 5. Informational and Placeholder Text: Text elements providing explanations about fact-checking, as well as placeholders in input fields to guide user input. 6. Toast Messages: Instant feedback to users through toast notifications about actions taken related to fact-checking. Overall, these string resources suggest that the application is enhancing its functionality to include comprehensive fact-checking features, allowing users to engage with, submit, and manage fact-checked information. Note : Based on limited code, the exact functionality of the Fact Check feature is not yet clear. #Factcheck#Communitynotes#Beta