[CVPR 2022] Structured Sparse R-CNN for Direct Scene Graph Generation

Related tags

Deep LearningStructured-Sparse-RCNN
Overview

Structured Sparse R-CNN for Direct Scene Graph Generation

Our paper Structured Sparse R-CNN for Direct Scene Graph Generation has been accepted by CVPR 2022.

Requirements

Environments: python 3.8, cuda 10.1, pytorch 1.7.1

To install requirements:

conda create --name scene_graph_benchmark
conda activate scene_graph_benchmark

pip install --user ipython
pip install --user scipy
pip install --user h5py
pip install --user pyyaml
pip install --user yacs
pip install --user scipy
pip install --user h5py
pip install --user tqdm
pip install --user opencv-python

pip install --user ninja yacs cython matplotlib tqdm opencv-python overrides


conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

# put our code SGGbench into your directory, such as /home/username
cd /home/username/SGGbench 
python setup.py build develop

Datasets

VG

For the dataset and pretrained backbone weights preparation, please follow: Scene-Graph-Benchmark.pytorch

OI v4, v6

For the datasets and pretrained backbone weights preparation, please follow: BGNN-SGG. Actually, BGNN-SGG is also compatible with Scene-Graph-Benchmark.pytorch.

Training

VG

On VG, we notice ~0.2 [email protected] noise for our model with 300 queries.

To train the model with 300 triplet queries in the paper, run this command. The results are [email protected]: 36.9; [email protected]: 3.7; [email protected]: 10.0: (8 RTX 2080ti 11G)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_train_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.PRETRAINED_DETECTOR_CKPT checkpoints/pretrained_faster_rcnn/model_final.pth OUTPUT_DIR Outputs/real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

To train the model with 800 triplet queries in the paper, run this command. The results are [email protected]: 38.4; [email protected]: 4.0; [email protected]: 10.3: (8 V100 32G)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_train_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.PRETRAINED_DETECTOR_CKPT checkpoints/pretrained_faster_rcnn/model_final.pth OUTPUT_DIR Outputs/real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 800 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

To train the model with 300 triplet queries and backbone, run this command. The results are [email protected]: 36.7; [email protected]: 3.8; [email protected]: 10.1: (8 V100 32G)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_train_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" SOLVER.BACKBONE_MULTIPLIER 0.1 MODEL.PRETRAINED_DETECTOR_CKPT checkpoints/pretrained_faster_rcnn/model_final.pth OUTPUT_DIR Outputs/real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel_bkb MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE False MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

OI V4

To train the model with 300 triplet queries in the paper, run this command: (8 RTX 2080ti 11G)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_train_net.py --config-file "configs/oiv4_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.PRETRAINED_DETECTOR_CKPT checkpoints/iov4_pretrain_faster_rcnn/model_final.pth OUTPUT_DIR Outputs/oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

OI V6

To train the model with 300 triplet queries in the paper, run this command: (8 RTX 2080ti 11G)

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_train_net.py --config-file "configs/oiv6_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.PRETRAINED_DETECTOR_CKPT checkpoints/iov6_pretrain_faster_rcnn/model_final.pth OUTPUT_DIR Outputs/oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

Evaluation

VG

300 queries

To evaluate my model, run:

CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/eva_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'eva_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with TDE, run:

CUDA_VISIBLE_DEVICES=2,3,4,5 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/tde_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS True MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'tde_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with our LA, run:

CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/la03_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True MODEL.SimrelRCNN.REL_LOGITS_ADJUSTMENT True MODEL.SimrelRCNN.LOGIT_ADJ_TAU 0.3

📋 Before testing, create a new directory, named 'la03_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

800 queries

To evaluate my model, run:

CUDA_VISIBLE_DEVICES=3,5,6,7 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/eva_real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 800 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'eva_real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with TDE, run:

CUDA_VISIBLE_DEVICES=2,3,4,5 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/tde_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 800 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS True MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'tde_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with our LA, run:

CUDA_VISIBLE_DEVICES=3,5,6,7 python -m torch.distributed.launch --master_port 10029 --nproc_per_node=4 tools/relation_test_net.py --config-file "configs/simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 4 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/LA03_real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 800 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 256 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True MODEL.SimrelRCNN.REL_LOGITS_ADJUSTMENT True MODEL.SimrelRCNN.LOGIT_ADJ_TAU 0.3

📋 Before testing, create a new directory, named 'LA03_real025kl_newe2rposition_fullobjbranch_800q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

OI V4

300 queries

To evaluate my model, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_test_net.py --config-file "configs/oiv4_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/eva_oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'eva_oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with our LA, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_test_net.py --config-file "configs/oiv4_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/la_oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True MODEL.SimrelRCNN.REL_LOGITS_ADJUSTMENT True MODEL.SimrelRCNN.LOGIT_ADJ_TAU 0.3

📋 Before testing, create a new directory, named 'la_oiv4_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

OI V6

300 queries

To evaluate my model, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_test_net.py --config-file "configs/oiv6_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/eva_oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True

📋 Before testing, create a new directory, named 'eva_oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

To evaluate my model with our LA, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --master_port 10020 --nproc_per_node=8 tools/relation_test_net.py --config-file "configs/oiv6_simrel_e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR CausalAnalysisPredictor MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_TYPE none MODEL.ROI_RELATION_HEAD.CAUSAL.FUSION_TYPE sum MODEL.ROI_RELATION_HEAD.CAUSAL.CONTEXT_LAYER motifs SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 SOLVER.VAL_PERIOD 12000 SOLVER.MAX_ITER 80000 SOLVER.STEPS '(47000, 64000)' SOLVER.BASE_LR 0.000008 SOLVER.OPTIMIZER "ADAMW" MODEL.WEIGHT Outputs/oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel/model_final.pth OUTPUT_DIR Outputs/la_oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM 1024 MODEL.SimrelRCNN.ENABLE_BG_OBJ False MODEL.SimrelRCNN.ENABLE_REL_X2Y True MODEL.SimrelRCNN.REL_DIM 256 MODEL.SimrelRCNN.NUM_PROPOSALS 300 MODEL.SimrelRCNN.NUM_HEADS 6 MODEL.SimrelRCNN.REL_STACK_NUM 6 MODEL.SimrelRCNN.TRIPLET_MASK_WEIGHT 1.0 MODEL.SimrelRCNN.FREEZE_BACKBONE True MODEL.SimrelRCNN.CROSS_OBJ_FEAT_FUSION False MODEL.SimrelRCNN.CLASS_WEIGHT 1.333 MODEL.SimrelRCNN.L1_WEIGHT 5.0 MODEL.SimrelRCNN.GIOU_WEIGHT 2.0 GLOVE_DIR '' MODEL.SimrelRCNN.ENABLE_FREQ False MODEL.SimrelRCNN.ENABLE_QUERY_REVERSE False MODEL.SimrelRCNN.USE_REFINE_OBJ_FEATURE True MODEL.SimrelRCNN.FREEZE_PUREE_OBJDET False MODEL.SimrelRCNN.PURE_ENT_NUM_PROPOSALS 100 MODEL.SimrelRCNN.ENABLE_MASK_BRANCH False MODEL.SimrelRCNN.KL_BRANCH_WEIGHT 0.25 MODEL.SimrelRCNN.ENABLE_KL_BRANCH True MODEL.SimrelRCNN.POSI_ENCODE_DIM 64 MODEL.SimrelRCNN.REL_CLASS_WEIGHT 1.334 MODEL.SimrelRCNN.PURE_ENT_CLASS_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_GIOU_WEIGHT 2.0 MODEL.SimrelRCNN.PURE_ENT_L1_WEIGHT 5.0 MODEL.SimrelRCNN.AUXILIARY_BRANCH True MODEL.SimrelRCNN.AUXILIARY_BRANCH_SELECT_ENT_MAX_NUM 25 MODEL.SimrelRCNN.AUXILIARY_BRANCH_START 11 MODEL.SimrelRCNN.ENABLE_ENT_PROP True MODEL.ROI_RELATION_HEAD.CAUSAL.EFFECT_ANALYSIS False MODEL.SimrelRCNN.USE_CROSS_RANK False MODEL.SimrelRCNN.DISABLE_KQ_FUSION_SELFATTEN False MODEL.SimrelRCNN.DIM_ENT_PRE_CLS 1024 MODEL.SimrelRCNN.DIM_ENT_PRE_REG 256 MODEL.SimrelRCNN.ONE_REL_CONV True MODEL.SimrelRCNN.ENABLE_BATCH_REDUCTION True MODEL.SimrelRCNN.USE_HARD_LABEL_KLMATCH True MODEL.SimrelRCNN.DISABLE_OBJ2REL_LOSS True MODEL.SimrelRCNN.REL_LOGITS_ADJUSTMENT True MODEL.SimrelRCNN.LOGIT_ADJ_TAU 0.3

📋 Before testing, create a new directory, named 'la_oiv6_real025kl_newe2rposition_fullobjbranch_300q_smalldyconv_prequeryaug9_norel_hardlabel', in ./SGGbench/Outputs/ .

Pre-trained Models

You can download pretrained models here:

VG

300 queries

800 queries

OI V4

300 queries

OI V6

300 queries

Results

Our model achieves the following performance on :

Visual Genome

* means 800 queries.

Models SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected] SGGen [email protected]
Our model 25.8 32.7 36.9 1.5 2.7 3.7 6.1 8.4 10.0
Our model* 26.1 33.5 38.4 1.5 2.7 4.0 6.2 8.6 10.3
Our model+TDE 14.5 18.3 21.0 1.8 2.7 3.6 10.8 15.0 18.5
Our model*+TDE 15.0 19.7 22.9 1.6 2.7 3.8 9.8 14.6 18.0
Our model+LA 18.4 23.3 26.5 1.9 2.9 4.0 13.5 17.9 21.4
Our model*+LA 18.2 23.7 27.3 2.0 3.1 4.5 13.7 18.6 22.5

Acknowledgement

Our code is mainly based on: Scene-Graph-Benchmark.pytorch, SparseR-CNN and BGNN-SGG.

For this paper, I'm extremely grateful to my advisor Prof. Limin Wang. I should also appreciate my group members for discussing with me: Jing Tan, Ziteng Gao and Jiaqi Tang.

Citations

@inproceedings{ssrcnnsgg22cvpr,
  author    = {Yao Teng and
               Limin Wang},
  title     = {Structured Sparse {R-CNN} for Direct Scene Graph Generation},
  booktitle = {{CVPR}},
  year      = {2022}
}
Owner
Multimedia Computing Group, Nanjing University
Multimedia Computing Group, Nanjing University
GitHub Repository
AI Toolkit for Healthcare Imaging

Medical Open Network for AI MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its am

Project MONAI 3.7k Jan 07, 2023
A mini-course offered to Undergrad chemistry students

The best way to use this material is by forking it by click the Fork button at the top, right corner. Then you will get your own copy to play with! Th

Raghu 19 Dec 19, 2022
A toolset for creating Qualtrics-based IAT experiments

Qualtrics IAT Tool A web app for generating the Implicit Association Test (IAT) running on Qualtrics Online Web App The app is hosted by Streamlit, a

0 Feb 12, 2022
MediaPipe is a an open-source framework from Google for building multimodal

MediaPipe is a an open-source framework from Google for building multimodal (eg. video, audio, any time series data), cross platform (i.e Android, iOS, web, edge devices) applied ML pipelines. It is

Bhavishya Pandit 3 Sep 30, 2022
Code for Boundary-Aware Segmentation Network for Mobile and Web Applications

BASNet Boundary-Aware Segmentation Network for Mobile and Web Applications This repository contain implementation of BASNet in tensorflow/keras. comme

Hamid Ali 8 Nov 24, 2022
DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.

ChemRxiv | [Paper] XXX DeepStruc Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and the

Emil Thyge Skaaning KjÊr 13 Aug 01, 2022
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

1 Jan 23, 2022
Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On

UPMT Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On See main.py as an example: from model import PopM

7 Sep 01, 2022
Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

73 Jan 01, 2023
(CVPR2021) Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

Kaleido-BERT: Vision-Language Pre-training on Fashion Domain Mingchen Zhuge*, Dehong Gao*, Deng-Ping Fan#, Linbo Jin, Ben Chen, Haoming Zhou, Minghui

248 Dec 04, 2022
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

67 Dec 21, 2022
SimpleDepthEstimation - An unified codebase for NN-based monocular depth estimation methods

SimpleDepthEstimation Introduction This is an unified codebase for NN-based monocular depth estimation methods, the framework is based on detectron2 (

8 Dec 13, 2022
Human Pose estimation with TensorFlow framework

Human Pose Estimation with TensorFlow Here you can find the implementation of the Human Body Pose Estimation algorithm, presented in the DeeperCut and

Eldar Insafutdinov 1.1k Dec 29, 2022
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022
This is the dataset and code release of the OpenRooms Dataset.

This is the dataset and code release of the OpenRooms Dataset.

Visual Intelligence Lab of UCSD 95 Jan 08, 2023
[ICML 2021] Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised

Michihiro Yasunaga 86 Nov 30, 2022
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

Denis 29 Nov 21, 2022
dataset for ECCV 2020 "Motion Capture from Internet Videos"

Motion Capture from Internet Videos Motion Capture from Internet Videos Junting Dong*, Qing Shuai*, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, Hujun Bao

ZJU3DV 98 Dec 07, 2022
Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION.

LiMuSE Overview Pytorch implementation of our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION. LiMuSE explores group communication on a multi

Auditory Model and Cognitive Computing Lab 17 Oct 26, 2022
Implementation for "Exploiting Aliasing for Manga Restoration" (CVPR 2021)

[CVPR Paper](To appear) | [Project Website](To appear) | BibTex Introduction As a popular entertainment art form, manga enriches the line drawings det

133 Dec 15, 2022