#javascript #addon #addons_mozilla_org #anti_tracking #chrome #firefox #privacy #security #tracking_protection #webextensions
https://github.com/ClearURLs/Addon
https://github.com/ClearURLs/Addon
GitHub
GitHub - ClearURLs/Addon: ClearURLs is an add-on based on the new WebExtensions technology and will automatically remove trackingโฆ
ClearURLs is an add-on based on the new WebExtensions technology and will automatically remove tracking elements from URLs to help protect your privacy. - ClearURLs/Addon
#other #adblock #adguard #ads #blacklist #blocklist #coins #dns #domains #fake #filterlist #hosts #malware #metrics #phishing #pi_hole #privacy #scam #telemetry #threat_intelligence_feeds #tracking
https://github.com/hagezi/dns-blocklists
https://github.com/hagezi/dns-blocklists
GitHub
GitHub - hagezi/dns-blocklists: DNS-Blocklists: For a better internet - keep the internet clean!
DNS-Blocklists: For a better internet - keep the internet clean! - hagezi/dns-blocklists
#javascript #anti_tracking #firefox #mozilla #privacy #security #settings #tracking
https://github.com/yokoffing/Betterfox
https://github.com/yokoffing/Betterfox
GitHub
GitHub - yokoffing/Betterfox: Firefox user.js for speed, privacy, and security. Turn off AI. Your favorite browser, but better.
Firefox user.js for speed, privacy, and security. Turn off AI. Your favorite browser, but better. - yokoffing/Betterfox
#python #deep_learning #hub #image_classification #instance_segmentation #machine_learning #obb #object_detection #pose #pytorch #tracking #ultralytics #yolo #yolo_world #yolo_world_v2 #yolo11 #yolov10 #yolov8 #yolov9
Ultralytics YOLO11 is a state-of-the-art model for object detection, segmentation, classification, and pose estimation. It is fast, accurate, and easy to use, making it suitable for various tasks. You can install it using pip (`pip install ultralytics`) and use it via the command line or Python scripts. The model comes with extensive documentation and community support through Discord, Reddit, and forums. Additionally, Ultralytics offers integrations with other AI platforms like Roboflow and ClearML to enhance your workflow. This tool benefits users by providing high-performance AI capabilities with minimal setup and robust community resources for assistance.
https://github.com/ultralytics/ultralytics
Ultralytics YOLO11 is a state-of-the-art model for object detection, segmentation, classification, and pose estimation. It is fast, accurate, and easy to use, making it suitable for various tasks. You can install it using pip (`pip install ultralytics`) and use it via the command line or Python scripts. The model comes with extensive documentation and community support through Discord, Reddit, and forums. Additionally, Ultralytics offers integrations with other AI platforms like Roboflow and ClearML to enhance your workflow. This tool benefits users by providing high-performance AI capabilities with minimal setup and robust community resources for assistance.
https://github.com/ultralytics/ultralytics
GitHub
GitHub - ultralytics/ultralytics: Ultralytics YOLO ๐
Ultralytics YOLO ๐. Contribute to ultralytics/ultralytics development by creating an account on GitHub.
#python #classification #coco #computer_vision #deep_learning #hacktoberfest #image_processing #instance_segmentation #low_code #machine_learning #metrics #object_detection #oriented_bounding_box #pascal_voc #python #pytorch #tensorflow #tracking #video_processing #yolo
Supervision is a powerful tool for building computer vision applications. It allows you to easily load datasets, draw detections on images or videos, and count detections in specific zones. You can use any classification, detection, or segmentation model with it, and it has connectors for popular libraries like Ultralytics and Transformers. Supervision also offers customizable annotators to visualize your data and utilities to manage datasets in various formats. By using Supervision, you can streamline your computer vision projects and make them more reliable and efficient. Additionally, there are extensive tutorials and documentation available to help you get started quickly.
https://github.com/roboflow/supervision
Supervision is a powerful tool for building computer vision applications. It allows you to easily load datasets, draw detections on images or videos, and count detections in specific zones. You can use any classification, detection, or segmentation model with it, and it has connectors for popular libraries like Ultralytics and Transformers. Supervision also offers customizable annotators to visualize your data and utilities to manage datasets in various formats. By using Supervision, you can streamline your computer vision projects and make them more reliable and efficient. Additionally, there are extensive tutorials and documentation available to help you get started quickly.
https://github.com/roboflow/supervision
GitHub
GitHub - roboflow/supervision: We write your reusable computer vision tools. ๐
We write your reusable computer vision tools. ๐. Contribute to roboflow/supervision development by creating an account on GitHub.
#python #botsort #bytetrack #deep_learning #deepocsort #improvedassociation #mot #mots #multi_object_tracking #multi_object_tracking_segmentation #ocsort #osnet #segmentation #strongsort #tensorrt #tracking_by_detection #yolo
BoxMOT is a tool that helps track multiple objects in videos or images using advanced models. It offers various tracking methods that work well on different types of hardware, from CPUs to powerful GPUs. This means you can use it even if your computer is not very powerful. BoxMOT also saves time by allowing you to reuse pre-generated data, so you don't have to repeat calculations every time. You can easily install and use it with popular object detection models like YOLOv8, YOLOv9, and YOLOv10, and it supports tracking different types of data such as bounding boxes, segmentation masks, and pose estimations. This makes it very flexible and useful for various tasks involving object tracking.
https://github.com/mikel-brostrom/boxmot
BoxMOT is a tool that helps track multiple objects in videos or images using advanced models. It offers various tracking methods that work well on different types of hardware, from CPUs to powerful GPUs. This means you can use it even if your computer is not very powerful. BoxMOT also saves time by allowing you to reuse pre-generated data, so you don't have to repeat calculations every time. You can easily install and use it with popular object detection models like YOLOv8, YOLOv9, and YOLOv10, and it supports tracking different types of data such as bounding boxes, segmentation masks, and pose estimations. This makes it very flexible and useful for various tasks involving object tracking.
https://github.com/mikel-brostrom/boxmot
GitHub
GitHub - mikel-brostrom/boxmot: BoxMOT: Pluggable SOTA multi-object tracking modules modules for segmentation, object detectionโฆ
BoxMOT: Pluggable SOTA multi-object tracking modules modules for segmentation, object detection and pose estimation models - mikel-brostrom/boxmot