Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (AAAI 2020)

YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming), Quanti…

利用realsense深度相机实现yolov5目标检测的同时测出距离

yolov5 prune，Support V2, V3, V4 and V6 versions of yolov5

mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！

yolov5 + csl_label.(Oriented Object Detection)（Rotation Detection）（Rotated BBox）基于yolov5的旋转目标检测

You Only Look Once for Panopitic Driving Perception.（MIR2022）

🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

Implementation for the paper 'YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs'

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

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

[NeurIPS 2021] Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO

Official PyTorch Implementation for "Rotate to Attend: Convolutional Triplet Attention Module." [WACV 2021]

PyTorch implementation of YOLOv4

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (AAAI 2020)

Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Depthwise Over-parameterized Convolutional Layer

A caffe implementation of MobileNet-YOLO detection network

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

⚡ 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 …

一次课程报告

使用粒子群算法优化的RBF神经网络进行预测。RBF neural network optimized by particle swarm optimization is used for prediction.

使用BP神经网络、RBF神经网络以及PSO优化的RBF神经网络进行数据的预测

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

利用stm32进行机械臂的制作与控制。

learn algorithm and note.

some notes and codes about learing algorithm using Java

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

PyTorch ,ONNX and TensorRT implementation of YOLOv4