(ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild"
[PDF] [Project webpage] [Code (Viewpoint)]
If our project is helpful for your research, please consider citing:
@INPROCEEDINGS{Xiao2020FSDetView,
author = {Yang Xiao and Renaud Marlet},
title = {Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2020}}
Code built on top of MetaR-CNN.
Requirements
- CUDA 8.0
- Python=3.6
- PyTorch=0.4.0
- torchvision=0.2.1
- gcc >= 4.9
Build
Create conda env:
conda create --name FSdetection --file spec-file.txt
conda activate FSdetectionCompile the CUDA dependencies:
cd {repo_root}/lib
sh make.shWe evaluate our method on two commonly-used benchmarks. Detailed data preparation commands can be found in data/README.md
We use the train/val sets of PASCAL VOC 2007+2012 for training and the test set of PASCAL VOC 2007 for evaluation. We split the 20 object classes into 15 base classes and 5 novel classes, and we consider 3 splits proposed in FSRW.
Download PASCAL VOC 2007+2012, create softlink named VOCdevkit in the folder data/.
We use COCO 2014 and keep the 5k images from minival set for evaluation and use the rest for training. We use the 20 object classes that are the same with PASCAL VOC as novel classes and use the rest as base classes.
Download COCO 2014, create softlink named coco in the folder data/.
Pre-trained ResNet:
we used ResNet101 pretrained model on ImageNet in our experiments.
Download it and put it into the data/pretrained_model/.
We provide pre-trained models of base-class training:
bash download_models.shYou will get a dir like:
save_models/
COCO/
VOC_first/
VOC_second/
VOC_third/
You can also train it yourself:
# the first split on VOC
bash run/train_voc_first.sh
# the second split on VOC
bash run/train_voc_second.sh
# the third split on VOC
bash run/train_voc_third.sh
# NonVOC / VOC split on CO
661A
CO
bash run/train_coco.shFine-tune the base-training models on a balanced training data including both base and novel classes:
bash run/finetune_voc_first.sh
bash run/finetune_voc_second.sh
bash run/finetune_voc_third.sh
bash run/finetune_coco.shEvaluation is conducted on the test set of PASCAL VOC 2007 or minival set of COCO 2014:
bash run/test_voc_first.sh
bash run/test_voc_second.sh
bash run/test_voc_third.sh
bash run/test_coco.sh