3090显卡跑Scene-Graph-Benchmark.pytorch(KaihuaTang)代码踩坑记录 Link: https://blog.csdn.net/m0_37867091/article/details/121429992 可能用到的一些文件的下载地址: https://aistudio.baidu.com/datasetdetail/18134 https://aistudio.baidu.com/datasetdetail/57396 展开 文件列表 VG-SGG-dicts.json attribute...
I decided to build a scene graph benchmark on top of the well-knownmaskrcnn-benchmarkproject and define relationship prediction as an additional roi_head. By the way, thanks to their elegant framework, this codebase is much more novice-friendly and easier to read/modify for your own project...
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch 大佬:“由于之前通用的SGG框架neural-motifs已经落后于时代,我设计了个新的代码框架(已于Github开源)。不仅结合了最新的maskrnn-benchmark用于底层物体检测,同时集成了目前最全的metrics包括Recall,Mean Recall,No Graph Constraint Recall, Zero Shot Recal...
Tang, K. (2020). A scene graph generation codebase in pytorch. (https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch) Tang, K., Niu, Y., Huang, J., Shi, J., & Zhang, H. (2020). Unbiased scene graph generation from biased training.IEEE conference on computer vision and pa...
/home/lkochiev/Documents/SFU/NSM/SGB/Scene-Graph-Benchmark.pytorch/ MODEL.PRETRAINED_DETECTOR_CKPT /home/lkochiev/checkpoints/pretrained_faster_rcnn/model_final.pth OUTPUT_DIR /home/lkochiev/checkpoints/motif-precls-exmpand strangely, I got a lot ofNO-MATCHING of current moduleandREMATCHING!
我们总共验证了两个benchmark。一个是最常用的50类关系的VG数据集,为了方便,我们在论文中简称VG-50 (其他论文里也有叫VG-150, VG-200的)。此外,我们还为大规模的关系检测专门划分了一个新的benchmarkVG-1800。 更可靠!VG-1800旨在为大规模关系检测的评测服务。我们手动过滤掉了不合理的关系,比如一些拼写错误,...
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch Recall@K for Iterative Message Passing(IMP), Neural Motifs, Transformer,VCTree Faster R-CNN预训练 该项目的Faster R-CNN预训练部分基本完全采用maskrcnn-benchmark的源代码(虽然我们又增加了attribute_head的实现,但还没开始正式使用),仅就数据集...
上表中,论文提出的SGG框架Scene-Graph-Benchmark.pytorch中重写的模型使用 † 标注。评估时,模型分别使用了两种融合函数以及九种推理方法,两种融合函数是:SUM、GATE,九种推理方法分别是:a. baseline b. 三种conventional debiasing methods:Focal、Reweight、Resample c. 两种intuitive causal graph surgeries:X2Y、X...
state-of-the-art results on the main semantic 3D scene graph benchmark by showing improved effectiveness over pre-training baselines and outperforming all the existing fully supervised scene graph prediction methods by a significant margin. Furthermore, since our scene graph features are language-...
• We propose a bi-level data resampling to achieve a bet- ter trade-off between head and tail categories for scene graph generation. • Our method achieves competitive or state-of-the-art performance on various scene graph benchmarks. 2. Related Works Sc...