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To compile Yolobit (YOLOv4) and generate an MP4 video, you need to follow specific steps. YOLOv4 is a state-of-the-art object detection model that can be used for various applications, including video processing.
Example
Clone the YOLOv4 repository: git clone https://github.com/AlexeyAB/darknet.git cd darknet
Compile the Darknet: make
Download the pre-trained weights: wget https://pjreddie.com/media/files/yolov4.weights
Run YOLOv4 on a video file: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename result.mp4
This command will process the test.mp4 video file and save the output as result.mp41.
Important Considerations
Dependencies: Ensure you have all necessary dependencies installed, such as OpenCV, CUDA, and cuDNN1.
Configuration Files: Make sure to use the correct configuration files (cfg/coco.data, cfg/yolov4.cfg) and weights (yolov4.weights)1.
Performance: Running YOLOv4 on high-resolution videos may require a powerful GPU to achieve real-time performance1.
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kiyoshiiriemon/yolov4_darknet: YOLOv4
TVM - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, …
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cap = cv2.VideoCapture("test2.mp4") # Local Stored video detection - Set input video
Using darknet_ros with Yolov3, how to test with mp4 file?
If you're trying to use this with an mp4 file you need to get that file publishing out as a video over ros. Right now the best, and really only, way to do this is via an opencv package. You can use …
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