Few-Shot Object Detection (Data Preparation)

First go to the data dir cd ./data

PASCAL VOC

Download data from official website:

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
tar xvf VOCtrainval_06-Nov-2007.tar && rm VOCtrainval_06-Nov-2007.tar 

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar && rm VOCtest_06-Nov-2007.tar

wget host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar xvf VOCtrainval_11-May-2012.tar && rm VOCtrainval_11-May-2012.tar 

Data structure should look like:

data/VOCdevkit
    VOC{2007,2012}/
        Annotations/
        ImageSets/
        JPEGImages/
        ...

Move the dataset split files to the correct path:

mv  VOCsplits/VOC2007/*  VOCdevkit/VOC2007/ImageSets/Main/
mv  VOCsplits/VOC2012/*  VOCdevkit/VOC2012/ImageSets/Main/  

COCO

Download data from official website:

mkdir coco

# download images
cd coco
mkdir images

wget images.cocodataset.org/zips/train2014.zip
unzip train2014.zip && rm train2014.zip
mv train2014 images/

wget images.cocodataset.org/zips/val2014.zip
unzip val2014.zip && rm val2014.zip
mv val2014 images/

# download annaotations
cd ..
wget images.cocodataset.org/annotations/annotations_trainval2014.zip
unzip annotations_trainval2014.zip && rm annotations_trainval2014.zip

Data structure should look like:

data/coco
    annotations/
    images/
        train2014/
        val2014/

Custom Dataset

To experiment with your own dataset, first save the dataset or create a soft link in the folder ProjectRootDir/data Moreover, you should have a csv-format file containing all the annotations such as:

set,im_path,cls,difficult,left,upper,right,lower,height,width
train,ImagePathInDataset,cat,False,x1,y1,x2,y2,H,W
train,ImagePathInDataset,dog,False,x1,y1,x2,y2,H,W
val,ImagePathInDataset,cat,False,x1,y1,x2,y2,H,W
val,ImagePathInDataset,dog,False,x1,y1,x2,y2,H,W
...

Then, look into factory.py to set up correctly the dataset root path and split names (train, val, etc).
Also, set up the novel classes for your custom dataset in the config.py

Once this is done, look further into
custom.py for dataset creation;
custom_metadata.py for few-shot class data creation;
custom_eval.py for evaluation.

Finally, you should set the correct values for custom dataset in train.py and test.py