Projects with this topic
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Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
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Ultralytics YOLO iOS app and Swift package for real-time Core ML inference across major computer vision tasks.
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Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.
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High-performance Ultralytics YOLO inference in Rust with ONNX Runtime, GPU backends, CLI, and WebGPU/WASM.
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Ultralytics YOLO26 quickstart for detection, instance and semantic segmentation, depth estimation, classification, pose, OBB, and tracking.
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Ultralytics YOLO11 discovery and quickstart for detection, segmentation, classification, pose, OBB, and tracking.
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Ultralytics YOLOv8 discovery and quickstart for detection, segmentation, classification, pose, OBB, and tracking.
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Ready-to-use Cog deployments and CI/CD for running Ultralytics YOLO11, YOLO World, YOLOE, and YOLO26 models on Replicate.
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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PyTorch sandbox for testing convolutional networks, ResNets, and other architectures on MNIST digits.
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An open-source computer vision framework for wildlife image analysis, featuring state-of-the-art models for species classification and detection.
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PyTorch reimplementation of CheXNet: multi-label classification and CAM/Grad-CAM localization of 14 thoracic diseases on ChestX-ray14, with full-resolution (1024px) ResNet50 training.
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HOG/LBP + SVM classifier
🖼️ , A comparative study of traditional computer vision features (HOG vs LBP) with SVM classifier for image classification.Updated -
Test project for neural networks - Handwritten digit recognition on MNIST dataset
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