Bottom Up AttentionBottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
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Unreal caffeSelf Maintained Caffe. In this version Faster-RCNN, RFCN needs layer are fully supported!
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Py Rfcn Privcode for py-R-FCN-multiGPU maintained by bupt-priv
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Caffe ModelCaffe models (including classification, detection and segmentation) and deploy files for famouse networks
Stars: ✭ 1,258 (+3958.06%)
crowd density segmentationThe code for preparing the training data for crowd counting / segmentation algorithm.
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R2CNNcaffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
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adversarial-attacksCode for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"
Stars: ✭ 90 (+190.32%)
deep-parkingCode to reproduce 'Deep Learning for Decentralized Parking Lot Occupancy Detection' paper.
Stars: ✭ 81 (+161.29%)
tf-faster-rcnnTensorflow 2 Faster-RCNN implementation from scratch supporting to the batch processing with MobileNetV2 and VGG16 backbones
Stars: ✭ 88 (+183.87%)
Faster-RCNN-LocNetA simplified implementation of paper : Improved Localization Accuracy by LocNet for Faster R-CNN Based Text Detection
Stars: ✭ 25 (-19.35%)
CFUNCombining Faster R-CNN and U-net for efficient medical image segmentation
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facial-landmarksFacial landmarks detection with OpenCV, Dlib, DNN
Stars: ✭ 25 (-19.35%)
pedestrian recognitionA simple human recognition api for re-ID usage, power by paper https://arxiv.org/abs/1703.07737
Stars: ✭ 29 (-6.45%)
caffeCaffe: a Fast framework for deep learning. Custom version with built-in sparse inputs, segmentation, object detection, class weights, and custom layers
Stars: ✭ 36 (+16.13%)
keras-faster-rcnnkeras实现faster rcnn,end2end训练、预测; 持续更新中,见todo... ;欢迎试用、关注并反馈问题
Stars: ✭ 85 (+174.19%)
Depth-VRDImproving Visual Relation Detection using Depth Maps (ICPR 2020)
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caffeThis fork of BVLC/Caffe is dedicated to supporting Cambricon deep learning processor and improving performance of this deep learning framework when running on Machine Learning Unit(MLU).
Stars: ✭ 40 (+29.03%)
lightDenseYOLOA real-time object detection app based on lightDenseYOLO Our lightDenseYOLO is the combination of two components: lightDenseNet as the CNN feature extractor and YOLO v2 as the detection module
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vqa-softAccompanying code for "A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models" CVPR 2017 VQA workshop paper.
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darknet2caffeConversion of yolo from DarkNet to Caffe
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XLearning-GPUqihoo360 xlearning with GPU support; AI on Hadoop
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superpixelRefinementSuperpixel-based Refinement for Object Proposal Generation (ICPR 2020)
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ppqPPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.
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TensorRT-LPR车牌识别,基于HyperLPR实现,修改模型调用方法,使用caffe+tensorRT实现GPU加速,修改了车牌检测模型
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TrainCaffeCustomDatasetTransfer learning in Caffe: example on how to train CaffeNet on custom dataset
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ddrlDeep Developmental Reinforcement Learning
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FDCNNThe implementation of FDCNN in paper - A Feature Difference Convolutional Neural Network-Based Change Detection Method
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caffe exampleinstall script and example for clCaffe which will run caffe by OpenCL (this is for https://github.com/01org/caffe/tree/inference-optimize)
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Classification NetsImplement popular models by different DL framework. Such as tensorflow and caffe
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YOLO-Object-Counting-APIThe code of the Object Counting API, implemented with the YOLO algorithm and with the SORT algorithm
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faster rcnnAnother pytorch implementation of Faster RCNN.
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object-trackingMultiple Object Tracking System in Keras + (Detection Network - YOLO)
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Caffe BEGANCaffe/C++ implementation of Boundary Equilibrium Generative Adversarial Networks paper for face generation
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odamODAM - Object detection and Monitoring
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dehaze[Preprint] "Improved Techniques for Learning to Dehaze and Beyond: A Collective Study"
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CenterNetPersonCenterNet used for pedestrian detection
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DLInfBenchCNN model inference benchmarks for some popular deep learning frameworks
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fooddistAn open-source food image embedding model
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PCN-WindowsNo description or website provided.
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