Faster R-CNN在COCO test-dev数据集上的mAP@.5是42.1%,比Fast R-CNN高2.8%;mAP@[.5,.95]的mAP@[.5,.95]是21.5%,比Fast R-CNN高2.2%。可以看到:Faster R-CNN的效果要优于Fast R-CNN,也说明了RPN网络的Excellent Performance. 评估指标mAP 论文中经常用mAP去衡量目标检测模型的好坏优劣,mAP的全称是Me...
Efficient: CenterNet-HarDNet85 model achieves 44.3 COCO mAP (test-dev) while running at 45 FPS on an NVIDIA GTX-1080Ti GPU. State of The Art: CenterNet-HarDNet85's is faster than YOLOv4, SpineNet-49, and EfficientDet-D2 Main results Object Detection on COCO validation Backbone#ParamGFLOPs...
Pelee: A Real-Time Object Detection System on Mobile Devices(NeurIPS 2018) The code is based on theSSDframework. If you find this work useful in your research, please consider citing: @incollection{NIPS2018_7466, title = {Pelee: A Real-Time Object Detection System on Mobile Devices}, autho...
同时,我们设计了一个传输连接块来传输锚点细化模块中的特征,以预测目标检测模块中目标的位置、大小和类标签。多任务丢失功能使我们能够以端到端方式训练整个网络。在PASCAL VOC 2007、PASCAL VOC 2012和MS COCO上的大量实验表明,RefineDet能够以高效的方式实现最先进的检测精度。
tensorflow object detection api一个框架,它可以很容易地构建、训练和部署对象检测模型,并且是一个提供了众多基于COCO数据集、Kitti数据集、Open Images数据集、AVA v2.1数据集和iNaturalist物种检测数据集上提供预先训练的对象检测模型集合。 tensorflow object detection api是目前最主流的目标检测框架之一,主流的目标检测...
5. COCO test 2017-dev detection results. BackboneAPAP0.5AP0.75APSAPMAP L Faster R-CNN ResNet-101 34.9 55.7 37.4 15.6 38.7 50.9 Faster R-CNN ResNet-101-FPN 36.2 59.1 39.0 18.2 39.0 48.2 Faster R-CNN∗ ResNet-50-FPN 36.2 58.5 38.9 21.0 38.9 45.3 Faster R-CNN∗ ResNet-101-FPN...
Weakly Supervised Object Detection Softer-NMS 2018 2019 Other Based on handong1587's github:https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html Survey Imbalance Problems in Object Detection: A Review intro: under review at TPAMI ...
Continue the discussion atforums.developer.nvidia.com 3 more replies Participants Experience the Ease of AI Model Creation with the TAO Toolkit on LaunchPad Creating an Object Detection Pipeline for GPUs DeepStream: Next-Generation Video Analytics for Smart Cities...
我们测试了不同的训练改进在 ImageNet 数据集分类任务 (ILSVRC 2012 年 val)和 MS COCO(test-dev 2017)数据集检测上 的准确性。 4.1. Experimental setup 实验设置 在ImageNet 的图像分类实验中,默认超参数如下:训练步数为 8 百万次;批大小和 mini 批大小分别为 128 和 32;polynomial decay learning rate sch...
Object Detection on COCO test-dev Leaderboard Dataset View by for BOX MAPFast-RCNNFast-RCNNSSD512SSD512Faster R-CNN (box refinement, context, multi-scale testing)Faster R-CNN (box refinement, context, multi-scale testing)Faster R-CNN + FPNFaster R-CNN + FPNFaster R-CNN ...