Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Scabbard | 766 | 6 months ago | 21 | apache-2.0 | Kotlin | |||||
🗡 A tool to visualize Dagger 2 dependency graphs | ||||||||||
Scene Graph Benchmark.pytorch | 760 | 8 months ago | 100 | mit | Jupyter Notebook | |||||
A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020” | ||||||||||
Graph Rcnn.pytorch | 482 | 3 years ago | 32 | Python | ||||||
Pytorch code for our ECCV 2018 paper "Graph R-CNN for Scene Graph Generation" and other papers | ||||||||||
Gran | 363 | a year ago | 7 | mit | C++ | |||||
Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019 | ||||||||||
Graphwriter | 222 | 3 years ago | 13 | Python | ||||||
Code for "Text Generation from Knowledge Graphs with Graph Transformers" | ||||||||||
Rl_graph_generation | 216 | 2 years ago | 7 | bsd-3-clause | Python | |||||
Beebug | 210 | 4 years ago | gpl-3.0 | Python | ||||||
A tool for checking exploitability | ||||||||||
Jngen | 189 | 4 months ago | 4 | mit | C++ | |||||
Library for generating tests for olympiad problems | ||||||||||
Ccm | 187 | 4 years ago | 6 | apache-2.0 | Python | |||||
This project is a tensorflow implement of our work, CCM. | ||||||||||
Amrlib | 161 | 2 | 2 months ago | 13 | March 08, 2022 | 4 | mit | Python | ||
A python library that makes AMR parsing, generation and visualization simple. |
Now in experimental release, suggestions welcome.
This is a Pytorch reimplementation of LinkNet for Scene Graph Generation.
Core code is rel_model_linknet.py, built on top of neural-motifs.
conda install pytorch=0.3.0 torchvision=0.2.0 cuda90 -c pytorch
make
in the main directory.export PYTHONPATH=/data/yjy/Workspace/linknet
CUDA_VISIBLE_DEVICES=0,1,2 python models/train_detector.py -b 6 -lr 1e-3 -save_dir checkpoints/vgdet -nepoch 50 -ngpu 3 -nwork 3 -p 100 -clip 5
CUDA_VISIBLE_DEVICES=0 python models/train_rels.py -m sgcls -model linknet -b 6 -clip 5 -p 100 -hidden_dim 256 -pooling_dim 4096 -lr 1e-3 -ngpu 1 -ckpt checkpoints/vgdet/vg-24.tar -save_dir checkpoints/linknet-sgcls -nepoch 50 -use_bias
CUDA_VISIBLE_DEVICES=0 python models/train_rels.py -m sgdet -model linknet -b 6 -clip 5 -p 100 -hidden_dim 256 -pooling_dim 4096 -lr 1e-4 -ngpu 1 -ckpt checkpoints/linknet-sgcls/vgrel-10.tar -save_dir checkpoints/linknet-sgdet -nepoch 10 -use_bias
CUDA_VISIBLE_DEVICES=0 python models/eval_rels.py -m predcls -model linknet -b 6 -clip 5 -p 100 -hidden_dim 256 -pooling_dim 4096 -lr 1e-3 -ngpu 1 -test -ckpt checkpoints/linknet-sgcls/vgrel-10.tar -nepoch 50 -use_bias -cache linknet_predcls
CUDA_VISIBLE_DEVICES=0 python models/eval_rels.py -m sgcls -model linknet -b 6 -clip 5 -p 100 -hidden_dim 256 -pooling_dim 4096 -lr 1e-3 -ngpu 1 -test -ckpt checkpoints/linknet-sgcls/vgrel-10.tar -nepoch 50 -use_bias -cache linknet_sgcls
CUDA_VISIBLE_DEVICES=0 python models/eval_rels.py -m sgdet -model linknet -b 6 -clip 5 -p 100 -hidden_dim 256 -pooling_dim 4096 -lr 1e-3 -ngpu 1 -test -ckpt checkpoints/linknet-sgdet/vgrel-18.tar -nepoch 50 -use_bias -cache linknet_sgdet
Mode | [email protected] | [email protected] | [email protected] |
---|---|---|---|
Predicate Classification | 58.8 | 65.5 | 67.4 |
Scene Graph Classification | 32.6 | 35.5 | 36.1 |
Scene Graph Detection | 13.6 | 20.5 | 25.0 |
For any question, please contact:
Jiayan Yang: [email protected]
Zhiwei Dong: [email protected]