Run the Experiment¶

Download Model¶

Pretrain weights¶

Download a pretrain weight you need from the following three options, and then put it to pretrained_weights.

MxNet pretrain weights (recommend in this repo, default in NET_NAME): resnet_v1d, resnet_v1b, refer to gluon2TF.
1. Baidu Drive (5ht9)
2. Google Drive
Tensorflow pretrain weights: resnet50_v1, resnet101_v1, resnet152_v1, efficientnet, mobilenet_v2, darknet53 (Baidu Drive (1jg2), Google Drive).
Pytorch pretrain weights, refer to pretrain_zoo.py and Others.
1. Baidu Drive (oofm)
2. Google Drive

Trained weights¶

Please download trained models by this project, then put them to trained_weights.

Compile¶

cd $PATH_ROOT/libs/utils/cython_utils
rm *.so
rm *.c
rm *.cpp
python setup.py build_ext --inplace (or make)

cd $PATH_ROOT/libs/utils/
rm *.so
rm *.c
rm *.cpp
python setup.py build_ext --inplace

Train¶

If you want to train your own dataset, please note:
1. Select the detector and dataset you want to use, and mark them as #DETECTOR and #DATASET (such as #DETECTOR=retinanet and #DATASET=DOTA)
2. Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py
3. Copy $PATH_ROOT/libs/configs/#DATASET/#DETECTOR/cfgs_xxx.py to $PATH_ROOT/libs/configs/cfgs.py
4. Add category information in $PATH_ROOT/libs/label_name_dict/label_dict.py
5. Add data_name to $PATH_ROOT/dataloader/dataset/read_tfrecord.py
Make tfrecord

If image is very large (such as DOTA dataset), the image needs to be cropped. Take DOTA dataset as a example:

cd $PATH_ROOT/dataloader/dataset/DOTA
python data_crop.py

If image does not need to be cropped, just convert the annotation file into xml format, refer to example.xml.

cd $PATH_ROOT/dataloader/dataset/
python convert_data_to_tfrecord.py --root_dir='/PATH/TO/DOTA/'
                                   --xml_dir='labeltxt'
                                   --image_dir='images'
                                   --save_name='train'
                                   --img_format='.png'
                                   --dataset='DOTA'

Start training

cd $PATH_ROOT/tools/#DETECTOR
python train.py

Train and Evaluation¶

For large-scale image, take DOTA dataset as a example (the output file or visualization is in $PATH_ROOT/tools/#DETECTOR/test_dota/VERSION):

cd $PATH_ROOT/tools/#DETECTOR
python test_dota.py --test_dir='/PATH/TO/IMAGES/'
                    --gpus=0,1,2,3,4,5,6,7
                    -ms (multi-scale testing, optional)
                    -s (visualization, optional)
                    -cn (use cpu nms, slightly better <1% than gpu nms but slower, optional)

or (recommend in this repo, better than multi-scale testing)

python test_dota_sota.py --test_dir='/PATH/TO/IMAGES/'
                         --gpus=0,1,2,3,4,5,6,7
                         -s (visualization, optional)
                         -cn (use cpu nms, slightly better <1% than gpu nms but slower, optional)

Note

In order to set the breakpoint conveniently, the read and write mode of the file is’ a+’. If the model of the same #VERSION needs to be tested again, the original test results need to be deleted.

For small-scale image, take HRSC2016 dataset as a example:

cd $PATH_ROOT/tools/#DETECTOR
python test_hrsc2016.py --test_dir='/PATH/TO/IMAGES/'
                        --gpu=0
                        --image_ext='bmp'
                        --test_annotation_path='/PATH/TO/ANNOTATIONS'
                        -s (visualization, optional)

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.