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Getting Started

This page provides basic usage based MMdetection. For installation instructions, please see install.md

Inferenece with pretrained models

We provide the testing scripts to evaluate the trained models.

Examples:

Assume that you have already downloaded the checkpoints to work_dirs/orientedreppoint_r50_demo/.

  1. Test OrientedRepPoints with single GPU.
python tools/test.py configs/dota/orientedrepoints_r50_demo.py \
    work_dirs/orientedreppoints_r50_demo/epoch_40.pth \ 
    --out work_dirs/orientedreppoints_r50_demo/results.pkl
  1. Test OrientedRepPoints with 4 GPUs.
./tools/dist_test.sh configs/dota/orientedrepoints_r50_demo.py \
    work_dirs/orientedreppoints_r50_demo/epoch_40.pth 4 \ 
    --out work_dirs/orientedreppoints_r50_demo/results.pkl

*If you want to evaluate the result on DOTA test-dev, please read the results.pkl, and run mergs the txt results. and zip the files and submit it to the evaluation server. *The code for parsing the .pkl file and merging the results for DOTA, please refer to this.

To evaluate on the val set with ground-truth annotations, please refer to DOTA_devkit/dota_evaluation_task1.py for mAP, and DOTA_devkit/mAOE_evaluation.py for mAOE.

Train a model

MMDetection implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel respectively.

All outputs (log files and checkpoints) will be saved to the working directory, which is specified by work_dir in the config file.

  1. Train with a single GPU
python tools/train.py ${CONFIG_FILE} 

example:
CUDA_VISIBLE_DEVICES=0 python toos/train.py configs/dota/orientedrepoints_r50_demo.py
  1. Train with multiple GPUs
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

example:
CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/dota/orientedrepoints_r50_demo.py 4