Starred repositories
statisticszhang / Deep-Learning-with-PyTorch-Tutorials
Forked from dragen1860/Deep-Learning-with-PyTorch-Tutorials深度学习与PyTorch入门实战视频教程 配套源代码和PPT
OpenCV Drowsiness Detector using Python and Dlib
Convert Cifar-100 to caffe's lmdb for caffe training
Caffe version Generalized & Distance & Complete Iou loss Implementation for Faster RCNN/FPN bbox regression
Frontend and backend separated object detection demo build with Flask, TensorFlow.
Code for our CVPR 2019 paper: Selective Kernel Networks; See zhihu：https://zhuanlan.zhihu.com/p/59690223
基于pytorch版ssd进行改进注入CBAM空间通道注意力机制，加入FPN，类别损失函数改为focalloss
Caffe version Generalized & Distance & Complete Iou loss Implementation for Faster RCNN/FPN bbox regression
Unofficial caffe implementation of Deformable ConvNet V1 and V2
Resnet-18、Resnet-34 and Resnet-50 etc. caffe train prototxt files.
Caffe Implementation of Google's MobileNets (v1 and v2)
Image classification caffe models , including train and deploy.prototxt files for famouse networks
Object detection papers and case realization，using caffe framework
算法工程师(人工智能CV方向)面试问题及相关资料
1
Updated Mar 13, 2020
A collection of various deep learning architectures, models, and tips
statisticszhang / deep-learning-papers-translation
Forked from SnailTyan/deep-learning-papers-translation深度学习论文翻译，包括分类论文，检测论文等
2
Updated May 22, 2020
ResNet-20/32/44/56/110 on CIFAR-10 with Caffe
Efficient Featurized Image Pyramid Network for Single Shot Detector, CVPR, 2019
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
Repulsion Loss: Detecting Pedestrians in a Crowd. https://arxiv.org/abs/1711.07752
[CVPR19] FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image
RT-GENE: Real-Time Eye Gaze and Blink Estimation in Natural Environments
This item is used for image recognition.
Fire and smoke classification and detection using efficientnet
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
MOT using deepsort and yolov3 with pytorch