Active Object Detection with Knowledge Aggregation and Distillation

This repository contains the code for the paper "Active Object Detection with Knowledge Aggregation and Distillation" accepted at CVPR 2024.

Requirements

• Python>=3.10.9
• torch>=1.13.1
• torchvision>=0.14.1
• mmcv>= 2.1.0
• mmdet>=3.3.0
• mmengine >=0.10.3
• timm>=0.6.13
• loguru
• requirements.txt file is provided for easy installation of the required packages.

Datasets

We evaluate our method on the following datasets:

• MECCANO
• 100DOH
• EPIC
• Ego4D

Split: fuqichen1998/SequentialVotingDet

Training

To train the teacher model, run the following command:

# for example [meccano]:
bash tools/dist_train.sh configs/active_object/meccano.py [num_gpus]

Evaluation

To evaluate the student model, run the following command:

# for example [meccano]:
bash tools/dist_test.sh configs/active_object/meccano.py [path_to_checkpoint] [num_gpus]

Checkpoints

	AP75	AP50	AP25	Models
Meccano	14.4	28.8	36.2	meccano
100DOH	31.2	53.9	58.9	100DOH
	AP	AP50	AP75	Models
ego4d-swin	40.5	60.6	41.9	ego4d-swin
ego4d-r50	31.4	34.6	28.9	ego4d-r50
epic-swin	35.2	44.1	32.5	epic-swin
epic-r50	30.2	30.1	22.5	epic-r50

Reference

If you find our work useful in your research, please consider citing our paper:

# TODO

Acknowledgements

We would like to thank the authors of mmdetection for providing the codebase for object detection.