Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6154–6162, 2018. Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: ...
To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin...
SwinTransformer/Swin-Transformer-Object-Detection Star1.8k This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation. object-detectioncascademscocomask-rcnnswinswin-transformerreppoints ...
Thanks for your work! I occured the error when I run the code. I run the command: python tools/train.py configs/swin/mydef_cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py --cfg-options model.pretrained=./models/cascade_mask_rcnn_swin_small...
同时,Hu等人[10]提出了SwinVRNN模型,该模型利用基于Swin Transformer的循环神经网络(RNN)(SwinRNN)模型与扰动模块相结合,基于变分自编码器框架学习多变量高斯分布。他们展示了SwinVRNN模型作为一个强大的基于机器学习的集合天气预报系统的潜力,其相比IFS在T2M和6小时TP上,在5.625°分辨率的5天预报中具有更好的成员...
正常性能，我试过cascade rcnn在小数据集几千张性能很优秀，相比faster rcnn高很多，分析主要一是小...
前天,arxiv上新出一篇论文《Cascade R-CNN: High Quality Object Detection and Instance Segmentation》,目标检测算法Cascade R-CNN 原作者对其进行扩展应用于实例分割。 两位作者均来自加州大学圣地亚哥分校,这可能是一篇投向TPAMI的论文。 在目标检测的实验中,借助于骨干网ResNeXt-152 的加持,在COCO数据集上AP达到50.9...
基于mmdet，加载coco预训练跑了两次检测比赛的数据，dino都是比cascade低1-2个点
cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x.py(your used config) config/base/model/cascade_mask_rcnn_swin_fpn.py config/base/coco_instance.py(or modify to coco_detection.py from 1) sure7018 commented Apr 30, 2021 What specific settings of mask ...
pretrainedpath = '/mnt/home/mmdetection/swin_large_patch4_window7_224_22k.pth' model = dict( type='CascadeRCNN', backbone=dict( type='SwinTransformer', embed_dims=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48],