789 Open source projects that are alternatives of or similar to Vehicle Detection

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To detect any reasonable change in a live cctv to avoid large storage of data. Once, we notice a change, our goal would be track that object or person causing it. We would be using Computer vision concepts. Our major focus will be on Deep Learning and will try to add as many features in the process.

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YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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Go bindings for Darknet (YOLO v4 / v3)

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Tensorflow implementation of YOLO, including training and test phase.

SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

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项目注释+论文复现+算法竞赛

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YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

计算机视觉相关论文整理、记录、分享; 包括图像分类、目标检测、视觉跟踪/目标跟踪、人脸识别/人脸验证、OCR/场景文本检测及识别等领域。欢迎加星,欢迎指正错误,同时也期待能够共同参与！！! 持续更新中... ...

A mobilenet SSD based face detector, powered by tensorflow object detection api, trained by WIDERFACE dataset.

Yet another yolov5, with its runtime stack for libtorch, onnx, tvm and specialized accelerators. You like torchvision's retinanet? You like yolov5? You love yolort!

This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.

Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)

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High level network definitions with pre-trained weights in TensorFlow

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A Caffe2 implementation of the YOLO v3 object detection algorithm

Inspired from Mask R-CNN to build a multi-task learning, two-branch architecture: one branch based on YOLOv2 for object detection, the other branch for instance segmentation. Simply tested on Rice and Shapes. MobileNet supported.

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