mayberpf's starred repositories
tensorRT_Pro
C++ library based on tensorrt integration
galaxy_camera
能启动多个大恒水星Gige相机,Can launch multi Gige Galaxy Camera with ROS
bilibili_project
和bilibili教学视频相关的一些源代码
unet-pytorch
这是一个unet-pytorch的源码,可以训练自己的模型
lanenet-lane-detection
Unofficial implemention of lanenet model for real time lane detection
MeanShift_py
Simple implementation of mean shift clustering in python
vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Data_Augmentation_Zoo_for_Object_Detection
Includes: Learning data augmentation strategies for object detection | GridMask data augmentation | Augmentation for small object detection in Numpy. Use RetinaNet with ResNet-18 to test these methods on VOC and KITTI.
DeepLabV3Plus-Pytorch
Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes
pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
CRF-semantic-segmentation
使用CRF进行语义分割
lidar_camera_calibration
ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"
camera_calibration
Accurate geometric camera calibration with generic camera models
CV_interviews_Q-A
CV算法岗知识点及面试问答汇总,主要分为计算机视觉、机器学习、图像处理和 C++基础四大块,一起努力向offers发起冲击!
Birds-eye-view-Perception
[IEEE T-PAMI] Awesome BEV perception research and cookbook for all level audience in autonomous diriving
deep-learning-for-image-processing
deep learning for image processing including classification and object-detection etc.
RepLKNet-pytorch
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (CVPR 2022)