CVPR2021-1616

Introduction

The code includes training and inference procedures for Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection.

Installation

Detection framework is based on the MMDetection v1.1.0.

Please refer to the Installation of MMDetection to complete the code environment.

Dataset

Please refer to DOTA to get the training, validation and test set.

Before training, the image-splitting process must be carried out. Check the DOTA_devkit.

Visualization Demo

We upload some validation images in demo/demo_datasets for visualization.

The detection results for these images is saved in demo/bbox_predict.pkl.

Use Jupyter Notebook to run demo/demo.ipynb to visualize the results.

Training and Inference

Create a training and inference shell script contains following command.

export OMP_NUM_THREADS=1
export CUDA_VISIBLE_DEVICES=0
GPUS=1

DIR=path_to_save_model
CONFIG=dota_configs/beyond_bounding_boxes_demo.py
./tools/dist_train.sh ${CONFIG} ${GPUS} --work_dir ${DIR} --gpus ${GPUS} --autoscale
./tools/dist_test.sh ${CONFIG} ${DIR}/latest.pth ${GPUS} --out ${DIR}/bbox_predict.pkl

Evaluation

DOTA_devkit supplies the evalution details.