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Name: jack
Type: User
Name: jack
Type: User
:zap: Based on yolo's ultra-lightweight universal target detection algorithm, the calculation amount is only 250mflops, the ncnn model size is only 666kb, the Raspberry Pi 3b can run up to 15fps+, and the mobile terminal can run up to 178fps+
Model Compression—YOLOv3 with multi lightweight backbones(ShuffleNetV2 HuaWei GhostNet), attention, prune and quantization
This is the implementation of YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝,通道剪枝,知识蒸馏
提供对YOLOv3及Tiny的多种剪枝版本,以适应不同的需求。
在 oxford hand 数据集上对 YOLOv3 做模型剪枝(network slimming)
Arbitrary-oriented object detection based on yolov3.
yolov3 model compress and acceleration (quantization, sparse), c++ version
Attempts to prune yolo v3 tiny.
YOLO ModelCompression MultidatasetTraining
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
YOLOv5 in DOTA with CSL_label.(Oriented Object Detection)(Rotation Detection)(Rotated BBox)基于YOLOv5的旋转目标检测
Clone of the mercurial repository http://zbar.hg.sourceforge.net:8000/hgroot/zbar/zbar
ZXing C++ Library
扫码 Zxing Zbar 结合 zxing 性能优化,极速秒扫
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