Implementation for the paper Few-shot Object Detection via Feature Reweighting
Our code is based on https://github.com/marvis/pytorch-yolo2 and developed with Python 3.6 & PyTorch 0.3.1.
$PROJ_ROOT : project root$DATA_ROOT : dataset root
- Get The Pascal VOC Data
cd $DATA_ROOT
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
- Generate Labels for VOC
wget http://pjreddie.com/media/files/voc_label.py
python voc_label.py
cat 2007_train.txt 2007_val.txt 2012_*.txt > voc_train.txt
- Generate per-class Labels for VOC (used for meta inpput)
cp $PROJ_ROOT/scripts/voc_label_1c.py $DATA_ROOT
cd $DATA_ROOT
python voc_label_1c.py
- Generate few-shot image list To use our few-shot datasets
cd $PROJ_ROOT
python scripts/convert_fewlist.py
You may want to generate new few-shot datasets Change the ''DROOT'' varibale in scripts/gen_fewlist.py to $DATA_ROOT
python scripts/gen_fewlist.py # might be different with ours
- Modify Cfg for Pascal Data Change the data/metayolo.data file
metayolo=1
metain_type=2
data=voc
neg = 1
rand = 0
novel = data/voc_novels.txt // file contains novel splits
novelid = 0 // which split to use
scale = 1
meta = data/voc_traindict_full.txt
train = $DATA_ROOT/voc_train.txt
valid = $DATA_ROOT/2007_test.txt
backup = backup/metayolo
gpus=1,2,3,4
- Download Pretrained Convolutional Weights
wget http://pjreddie.com/media/files/darknet19_448.conv.23
- Train The Model
python train_meta.py cfg/metayolo.data cfg/darknet_dynamic.cfg cfg/reweighting_net.cfg darknet19_448.conv.23
- Evaluate the Model
python valid_ensemble.py cfg/metayolo.data cfg/darknet_dynamic.cfg cfg/reweighting_net.cfg path/toweightfile
python scripts/voc_eval.py results/path/to/comp4_det_test_
- Modify Cfg for Pascal Data Change the data/metatune.data file
metayolo=1
metain_type=2
data=voc
tuning = 1
neg = 0
rand = 0
novel = data/voc_novels.txt
novelid = 0
max_epoch = 2000
repeat = 200
dynamic = 0
scale=1
train = $DATA_ROOT/voc_train.txt
meta = data/voc_traindict_bbox_5shot.txt
valid = $DATA_ROOT/2007_test.txt
backup = backup/metatune
gpus = 1,2,3,4
- Train The Model
python train_meta.py cfg/metatune.data cfg/darknet_dynamic.cfg cfg/reweighting_net.cfg path/to/base/weightfile
- Evaluate the Model
python valid_ensemble.py cfg/metatune.data cfg/darknet_dynamic.cfg cfg/reweighting_net.cfg path/to/tuned/weightfile
python scripts/voc_eval.py results/path/to/comp4_det_test_
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