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GitHub - hertasecurity/gpu-nms: This repository contains the CUDA implementation of the paper "Work-efficient Parallel Non-Maximum Suppression Kernels".
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This repository contains the CUDA implementation of the paper "Work-efficient Parallel Non-Maximum Suppression Kernels".
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hertasecurity/gpu-nms
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=================================================================================== NMS Benchmarking Framework =================================================================================== CUDA implementation of the algorithm described in the paper: "Work-Efficient Parallel Non-Maximum Suppression Kernels" https://dx.doi.org/10.1093/comjnl/bxaa108 The Computer Journal David Oro, Carles Fernández, Xavier Martorell, Javier Hernando =================================================================================== * Requirements: 1. GCC Compiler v5.0 or greater 2. CUDA Toolkit v6.0 or greater 3. NVIDIA GPU with Compute Capability 3.2 or greater * Build instructions: 1. Set the GPU_ARCH and SM_ARCH variables in the Makefile according to the underlying NVIDIA GPU architecture of your computer. For further details, please refer to our GitHub Wiki page: https://github.com/hertasecurity/gpu-nms/wiki 2. Set your CUDA installation path in the Makefile (CUDA_HEADERS and CUDA_LIBS variables) 3. Compile the source code: make * Execution: * You can run the GPU NMS benchmark using a comma-separated input file containing the list of detected objects in the following format: xcoordinate,ycoordinate,width,score * We provide a sample input file "detections.txt" obtained after having executed a face detector over the "oscars.png" file. * The GPU NMS benchmark must be executed as follows: ./nmstest detections.txt output.txt * The application should then return the computation time of both the MAP and REDUCE GPU NMS kernels and write the results in the "output.txt" file. * Finally, you can visualize both the input (pre-NMS) and the output (post-NMS) with the "drawrectangles" Python script. For example: ./drawrectangles detections.txt Or: ./drawrectangles output.txt The graphical output is stored in the "oscarsdets.png" file * IMPORTANT: * The source code must be compiled to the microarchitecture matching the GPU platform during execution (check GPU_ARCH and SM_ARCH variables in the Makefile). * If the NMS algorithm is not capable of properly merging the candidate windows, re-check the GPU_ARCH and SM_ARCH variables and then recompile the code. * This GPU NMS benchmark is limited to a maximum of 4096 detected objects per input. If you want to increase this limit, please modify the MAX_DETECTIONS constant in the "nms.cu" file.
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This repository contains the CUDA implementation of the paper "Work-efficient Parallel Non-Maximum Suppression Kernels".
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