Ultralytics
@ultralytics
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Simpler. Smarter. Further.
Frederick, MD
Joined February 2014
Ultralytics YOLO11 is here! 🚀 As unveiled at YOLO Vision 2024, our new models is now live in the Ultralytics Python package! Featuring: ✅ Precise detection & complex tasks ✅ Detection, segmentation, pose & obb 🔗Learn more:
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Using Ultralytics YOLO11 for counting pills! 💊 Whether you’re ensuring quality control in pharmaceutical production or optimizing the sorting and packaging process, using Ultralytics can simplify your workload. Learn more ➡️
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Number plate recognition (ANPR) with Ultralytics YOLO11 & GPT-4o Mini! 🚀 Check out Abirami Vina's latest blog as we explore how to build an ANPR system using Ultralytics YOLO11 for plate detection and GPT-4o Mini for text recognition. Learn more ➡️ #AI
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🚀 Ultralytics v8.3.74 is live! ✨ Highlights: - 🔧 Fixed Ray Tune callback issues - 🛠 Improved deterministic training - 📸 Added PIL image support #AI #ML #Ultralytics
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Boost your detection accuracy with Ultralytics YOLO11 & SAHI 🚀 By slicing large images, SAHI enhances YOLO11's ability to detect small, dense objects. Check out the visual comparison below.👇 Learn more➡️ #YOLO11 #SAHI #ObjectDetection #DeepLearning
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Code 👇 """" from ultralytics import YOLO # Load a model model = YOLO(") # load an official model # Predict with the model results = model(") # predict on an image """"
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RT @seeedstudio: 🚀 One-click local deployment: now you can easily run @Ultralytics #YOLO models on #reComputer Jetson! Check out our Jetso…
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RT @muhammdrizwanmr: Luggage counting at the airport using @ultralytics YOLO11🧳 Fine-tuned YOLO11 model on the suitcase dataset has been u…
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A look at Ultralytics YOLO11 in a logistic scenario! 📊 Whether you’re improving safety in warehouses or streamlining transportation processes, Ultralytics has you covered. Learn more ➡️ #AI
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Using computer vision for underwater detection! 🌊 Abdelrahman Elgendy's new blog dives into how vision AI can reshape marine monitoring with models like Ultralytics YOLO11 making processes faster, smarter, and more scalable. Learn more ➡️ #AI
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Package identification and segmentation with Ultralytics YOLO11! 📦 @AbiramiVina's latest blog explores how Ultralytics YOLO11 can be custom-trained using the @roboflow Package Segmentation Dataset to improve logistics workflows. Learn more ➡️
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🚀 Ultralytics `v8.3.73` is here! 🐋 Docker images now on GHCR & Docker Hub 🤖 Updated NVIDIA Jetson support w/ PyTorch 2.2.0 🎥 New YouTube tutorial in docs #AI #MachineLearning
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Code 👇 """" import cv2 from ultralytics import solutions cap = cv2.VideoCapture("Path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # Video writer video_writer = cv2.VideoWriter("heatmap_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) # In case you want to apply object counting + heatmaps, you can pass region points. # region_points = [(20, 400), (1080, 400)] # Define line points # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # Define region points # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360), (20, 400)] # Define polygon points # Init heatmap heatmap = solutions.Heatmap( show=True, # Display the output model=", # Path to the YOLO11 model file colormap=cv2.COLORMAP_PARULA, # Colormap of heatmap # region=region_points, # If you want to do object counting with heatmaps, you can pass region_points # classes=[0, 2], # If you want to generate heatmap for specific classes i.e person and car. # show_in=True, # Display in counts # show_out=True, # Display out counts # line_width=2, # Adjust the line width for bounding boxes and text display ) # Process video while cap.isOpened(): success, im0 = ) if not success: print("Video frame is empty or video processing has been successfully completed.") break im0 = heatmap.generate_heatmap(im0) video_writer.write(im0) cap.release() video_writer.release() cv2.destroyAllWindows() """"
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RT @muhammdrizwanmr: Blur face in digital ads with @ultralytics YOLO11! ❤️🔥 I used the face detection model with object blurring feature!…
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Maximize efficiency with computer-vision-driven product counting on factory lines! 🏭 Optimize production, reduce errors, and boost productivity with Ultralytics YOLO11. Learn more ➡️ #AI #Automation #ManufacturingTech #SmartFactory
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Computer vision is helping in civil engineering! 🚧 Abdelrahman Elgendy's new blog dives into how Vision AI models like Ultralytics YOLO11 can help on construction sites, from detecting vehicles to ensuring PPE compliance. Learn more ➡️ #AI
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