Few-shot Object Detection via Feature Reweighting (Remake Version)

This repo is the remake version for the original code of https://github.com/bingykang/Fewshot_Detection .

If any problems, open an issue!

What I Have Done:

This code has been upgraded to work with python3.6+, pytorch1.2+ which is more suitable for nowadays settings.
Reorganize the code and add some annotation to understand the code.
Modify the ouput information, which is more simple and efficient.

Additional Details:

dataset dir

In this code, you have to prepare dataset as the original code did. Please remember to change the root dir of text file in the ./data/ path, such as voc_traindict_bbox_1shot.txt.

result of k-shot k=1,2,5,10

the base training part is always the same for different k, but the finetune part is different. To check the result of k-shot(k=1,2,5,10), you have to find the file: ./cfg/metatune.data

find the code meta = data/voc_traindict_bbox_5shot.txt and change the num 5 to 1 or others.

why proposal is decreased to 0?

Don't worry! It will increase after several epochs.

memory usage / cuda out of memory

Since the detection part is based on yolov2, multi-scale training is used. After the beginning few epochs(maybe 20), the input shape will change from (416,416) to (512,512) or random shape. Choose an adaptive batch size is very important!.

Original Readme

Implementation for the paper:

Few-shot Object Detection via Feature Reweighting, ICCV 2019

Bingyi Kang*, Zhuang Liu*, Xin Wang, Fisher Yu, Jiashi Feng and Trevor Darrell (* equal contribution)

Our code is based on https://github.com/marvis/pytorch-yolo2 and developed with Python 2.7 & PyTorch 0.3.1.

Training our model on VOC

$PROJ_ROOT : project root
$DATA_ROOT : dataset root

Prepare dataset

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

Base Training

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_

Few-shot Tuning

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