Ubuntu 16.04
Anaconda3
withpython=3.6
Pytorch=1.9.0
mmdetection=2.16.0+fe46ffe
mmcv=1.3.9
wandb=0.10.31
- We use wandb for visualization, if you don't want to use it, just comment line
276-289
inconfigs/soft_teacher/base.py
.
make install
- Download the COCO dataset
- Execute the following command to generate data set splits:
# YOUR_DATA should be a directory contains coco dataset.
# For eg.:
# YOUR_DATA/
# coco/
# train2017/
# val2017/
# unlabeled2017/
# annotations/
ln -s ${YOUR_DATA} data
bash tools/dataset/prepare_coco_data.sh conduct
- To train model on the partial labeled data setting:
# JOB_TYPE: 'baseline' or 'semi', decide which kind of job to run
# PERCENT_LABELED_DATA: 1, 5, 10. The ratio of labeled coco data in whole training dataset.
# GPU_NUM: number of gpus to run the job
for FOLD in 1 2 3 4 5;
do
bash tools/dist_train_partially.sh <JOB_TYPE> ${FOLD} <PERCENT_LABELED_DATA> <GPU_NUM>
done
For example, we could run the following scripts to train our model on 10% labeled data with 8 GPUs:
for FOLD in 1 2 3 4 5;
do
bash tools/dist_train_partially.sh semi ${FOLD} 10 8
done
- To train model on the full labeled data setting:
bash tools/dist_train.sh <CONFIG_FILE_PATH> <NUM_GPUS>
For example, to train ours R50
model with 8 GPUs:
bash tools/dist_train.sh configs/soft_teacher/soft_teacher_faster_rcnn_r50_caffe_fpn_coco_full_720k.py 8
bash tools/dist_test.sh <CONFIG_FILE_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval bbox --cfg-options model.test_cfg.rcnn.score_thr=<THR>