Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)

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Deep Learning Dsig
Overview

DSIG

Deep Structured Instance Graph for Distilling Object Detectors

Authors: Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, Jiaya Jia.

[pdf] [slide] [supp] [bibtex]

This repo provides the implementation of paper "Deep Structured Instance Graph for Distilling Object Detectors"(Dsig) based on detectron2. Specifically, aiming at solving the feature imbalance problem while further excavating the missing relation inside semantic instances, we design a graph whose nodes correspond to instance proposal-level features and edges represent the relation between nodes. We achieve new state-of-the-art results on the COCO object detection task with diverse student-teacher pairs on both one- and two-stage detectors.

Installation

Requirements

  • Python >= 3.6
  • Pytorch >= 1.7.0
  • Torchvision >= 0.8.1
  • Pycocotools 2.0.2

Follow the install instructions in detectron2, note that in this repo we use detectron2 commit version ff638c931d5999f29c22c1d46a3023e67a5ae6a1. Download COCO dataset and export DETECTRON2_DATASETS=$COCOPATH to direct to COCO dataset. We prepare our pre-trained weights for training in Student-Teacher format, please follow the instructions in Pretrained.

Running

We prepare training configs following the detectron2 format. For training a Faster R-CNN R18-FPN student with a Faster R-CNN R50-FPN teacher on 4 GPUs:

./start_train.sh train projects/Distillation/configs/Distillation-FasterRCNN-R18-R50-dsig-1x.yaml

For testing:

./start_train.sh eval projects/Distillation/configs/Distillation-FasterRCNN-R18-R50-dsig-1x.yaml

For debugging:

./start_train.sh debugtrain projects/Distillation/configs/Distillation-FasterRCNN-R18-R50-dsig-1x.yaml

Results and Models

Faster R-CNN:

Experiment(Student-Teacher) Schedule AP Config Model
R18-R50 1x 37.25 config googledrive
R50-R101 1x 40.57 config googledrive
R101-R152 1x 41.65 config googledrive
MNV2-R50 1x 34.44 config googledrive
EB0-R101 1x 37.74 config googledrive

RetinaNet:

Experiment(Student-Teacher) Schedule AP Config Model
R18-R50 1x 34.72 config googledrive
MNV2-R50 1x 32.16 config googledrive
EB0-R101 1x 34.44 config googledrive

More models and results will be released soon.

Citation

@inproceedings{chen2021dsig,
    title={Deep Structured Instance Graph for Distilling Object Detectors},
    author={Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, and Jiaya Jia},
    booktitle={IEEE International Conference on Computer Vision (ICCV)},
    year={2021},
}

Contact

Please contact [email protected].

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Comments
  • FastRCNNOutputs

    FastRCNNOutputs

    I am trying to reproduce your code, why is this error being reported during training? I looked at the file fast_rcnn.py and I really didn't find 'FastRCNNOutputs'? What is the reason for this? ImportError: cannot import name 'FastRCNNOutputs' from 'detectron2.modeling.roi_heads.fast_rcnn' (/project/Dsig-main/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py)

    opened by Stlve 0
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