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-#First Readme
+# Within-Triplet CRF for Dynamic Scene Graph Generation
+We propose a Within-Triplet Transformer-based CRF model **WT-CRF** to generate dynamic scene graphs of the given video. **WT-CRF** computes the unary and temporal potential of a relationship pair given the local-global within-triplet features and combines these potentials in a Conditional Random Field (CRF) framework. 
 
 
+**About the code**
 
+We borrowed the repo from  [Cong's repository](https://github.com/yrcong/STTran)
+
+## Requirements
+- python=3.6
+- pytorch=1.1
+- scipy=1.1.0
+- torchvision=0.3
+- cypthon
+- dill
+- easydict
+- h5py
+- opencv
+- pandas
+- tqdm
+- yaml
+```
+
+We borrow some compiled code for bbox operations.
+```
+cd lib/draw_rectangles
+python setup.py build_ext --inplace
+cd ..
+cd fpn/box_intersections_cpu
+python setup.py build_ext --inplace
+```
+For the object detector part, please follow the compilation from https://github.com/jwyang/faster-rcnn.pytorch
+We provide a pretrained FasterRCNN model for Action Genome. Please download [here](https://drive.google.com/file/d/1-u930Pk0JYz3ivS6V_HNTM1D5AxmN5Bs/view?usp=sharing) and put it in 
+```
+fasterRCNN/models/faster_rcnn_ag.pth
+```
+
+## Dataset
+We use the dataset [Action Genome](https://www.actiongenome.org/#download) to train/evaluate our method. Please process the downloaded dataset with the [Toolkit](https://github.com/JingweiJ/ActionGenome). The directories of the dataset should look like:
+```
+|-- action_genome
+    |-- annotations   #gt annotations
+    |-- frames        #sampled frames
+    |-- videos        #original videos
+```
+ In the experiments for SGCLS/SGDET, we only keep bounding boxes with short edges larger than 16 pixels. Please download the file [object_bbox_and_relationship_filtersmall.pkl](https://drive.google.com/file/d/19BkAwjCw5ByyGyZjFo174Oc3Ud56fkaT/view?usp=sharing) and put it in the ```dataloader```
+
+## Train
+You can train the **WT-CRF** with train.py. We trained the model on a RTX 2080ti:
++ For PredCLS: 
+```
+python train.py -mode predcls -datasize large -data_path $DATAPATH 
+```
++ For SGCLS: 
+```
+python train.py -mode sgcls -datasize large -data_path $DATAPATH 
+```
++ For SGDET: 
+```
+python train.py -mode sgdet -datasize large -data_path $DATAPATH 
+```
+
+## Evaluation
+You can evaluate the **STTran** with test.py.
++ For PredCLS ([trained Model](https://drive.google.com/file/d/1Sk5qFLWTZmwr63fHpy_C7oIxZSQU16vU/view?usp=sharing)): 
+```
+python test.py -m predcls -datasize large -data_path $DATAPATH -model_path $MODELPATH
+```
++ For SGCLS ([trained Model](https://drive.google.com/file/d/1ZbJ7JkTEVM9mCI-9e5bCo6uDlKbWttgH/view?usp=sharing)): : 
+```
+python test.py -m sgcls -datasize large -data_path $DATAPATH -model_path $MODELPATH
+```
++ For SGDET ([trained Model](https://drive.google.com/file/d/1dBE90bQaXB-xogRdyAJa2A5S8RwYvjPp/view?usp=sharing)): : 
+```
+python test.py -m sgdet -datasize large -data_path $DATAPATH -model_path $MODELPATH
+```
+
+## Citation
+If our work is helpful for your research, please cite our publication:
+```
+@inproceedings{cong2021spatial,
+  title={Spatial-Temporal Transformer for Dynamic Scene Graph Generation},
+  author={Cong, Yuren and Liao, Wentong and Ackermann, Hanno and Rosenhahn, Bodo and Yang, Michael Ying},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={16372--16382},
+  year={2021}
+}
+```
+## Help 
+When you have any question/idea about the code/paper. Please comment in Github or send us Email. We will reply as soon as possible.