NEW - YOLOv8 π in PyTorch > ONNX > OpenVINO > CoreML > TFLite
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Updated
May 29, 2024 - Python
NEW - YOLOv8 π in PyTorch > ONNX > OpenVINO > CoreML > TFLite
i have used 6 defect types which are common in pipeline defect detection.
YOLOv5 π in PyTorch > ONNX > CoreML > TFLite
AI-First Process Automation with Large ([Language (LLMs) / Action (LAMs) / Multimodal (LMMs)] / Visual Language (VLMs)) Models
Profile for Glenn Jocher
Ultralytics GitHub Actions
MkDocs plugin for Ultralytics Docs at https://docs.ultralytics.com
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
Ultralytics HUB tutorials and support
xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
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Ultralytics YOLO iOS App source code for running YOLOv8 in your own iOS apps π
Ultralytics Docs at https://docs.ultralytics.com/
Quickly detect and classify different species of harmful algae within natural water in real-time with AI and a camera (i.e., ESP32-CAM, smartphone, or webcam).
An open-source project dedicated to constructing robust data pipelines and scalable software infrastructure. We leverage industry-standard tools favored by developers to enhance efficiency and reliability. Uniquely, these pipelines are field-tested on farms across Sumatra, Indonesia, ensuring real-world applicability and resilience.
Object recognition in photo captured from webcam
This project leverages a custom-trained YOLOv9 model to detect objects related to room cleanliness. Built with Gradio, it provides an interactive web interface where users can upload images and adjust detection parameters. The app returns images with annotated bounding boxes around detected objects, aiding in room organization tasks.
Object Detection, Object Segmentation, and Pose Detection with Tracking Using the Official Models of Ultralytics YOLOv8.
πππ A collection of some awesome public YOLO object detection series projects.
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