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YOLO

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YOLO (You Only Look Once) is a real-time object detection system that processes images at 30 FPS and has a mAP of 57.9% on a Pascal Titan X, and it can be trained on different datasets such as Pascal VOC and Open Images

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  • 💻 Real-time object detection: YOLOv3 is a system that can detect objects in real-time, making it useful for applications such as surveillance and autonomous vehicles.
  • 📈 Improved training and performance: YOLOv3 uses multi-scale predictions, a better backbone classifier, and other tricks to improve training and increase performance.
  • 🤖 Pre-trained model: YOLOv3 comes with a pre-trained model that can be used for object detection without the need for extensive training.
  • 🌎 Cross-platform compatibility: YOLOv3 is compatible with multiple platforms, including Linux, Windows, and macOS.
  • 📄 Detailed documentation: The full details of YOLOv3’s features and performance improvements are available in the system’s paper.

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FAQ

To detect objects with YOLO using a pre-trained model, you need to have Darknet installed. You can then download the pre-trained weight file and run the following command: ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights
To train YOLO from scratch, you need to have Darknet installed and the Pascal VOC dataset. You can then run the following command: ./darknet detector train cfg/coco.data cfg/yolov3.cfg darknet53.conv.74 -gpus 0,1,2,3. This will train the YOLOv3 model on the Pascal VOC dataset.
To run YOLO on a webcam, you need to have Darknet compiled with CUDA and OpenCV. You also need to pick a YOLO config file and have the appropriate weights file. You can then run the following command: ./darknet yolo demo cfg/yolov1/yolo.cfg yolov1.weights. This will run YOLO on the input from a webcam and display the current FPS and predicted classes as well as the image with bounding

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