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:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+
Support Yolov5s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
yolov3 yolov4 channel and layer pruning, Knowledge Distillation 层剪枝,通道剪枝,知识蒸馏
YOLO ModelCompression MultidatasetTraining
This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO
Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net
YOLOv5 in PyTorch > ONNX > CoreML > iOS
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
YOLOv5 in DOTA with CSL_label.(Oriented Object Detection)(Rotation Detection)(Rotated BBox)基于YOLOv5的旋转目标检测
🍅 Deploy NCNN on mobile phones. Support Android and iOS.
yolov5剪枝,支持v2,v3,v4版本的yolov5
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
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