Skip to content

Latest commit

 

History

History
186 lines (156 loc) · 7.38 KB

README.md

File metadata and controls

186 lines (156 loc) · 7.38 KB
Raw

OICR-PyTorch

This repository contains the PyTorch implementation of paper Multiple Instance Detection Network with Online Instance Classifier Refinement (CVPR 2017)

Related code: WSDDN PyTorch

Architecture

OICR

Results

VOC2007 test

aero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tv mAP
Ap 61.1 67.9 42.8 13.0 12.5 67.2 66.7 38.5 20.3 49.5 35.3 28.5 33.8 67.4 5.7 20.5 41.7 42.6 62.1 67.3 42.2

VOC2007 trainval

aero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tv mean
CorLoc 80.4 82.7 67.3 42.6 41.2 80.2 85.5 51.5 42.7 78.8 43.3 40.5 52.0 88.4 15.7 57.1 81.4 53.2 74.1 82.8 62.1

Contents

  1. Requirements: software
  2. Requirements: hardware
  3. Basic installation
  4. Installation for training and testing
  5. Extra Downloads (selective search)
  6. Extra Downloads (ImageNet models)
  7. Usage

Requirements: software

Python3 packages and versions used (listed using freeze frin pip) are in requirements.txt.

You can create a new virtual environment and then install thses packages from requirements.txt.

conda create -n env_name python=3.6
pip install -r $OICR_ROOT/requirements.txt

You can also install these packages by yourself.

Besides, you should install Octave, which is mostly compatible with MATLAB.

sudo apt-get install octave

Requirements: hardware

  • We used cuda 9.0 and cudnn 7.0 on Ubuntu 16.04
    • We used an Nvidia GeForce GTX with 10.9G of memory. But it shold be ok to train if you have a GPU with at least 8Gb.
    • NOTICE: different versions of Pytorch have different memory usages.

Basic installation

Clone this repository

git clone https://github.com/CatOneTwo/OICR-PyTorch

Installation for training and testing

  1. Create a "data" folder in $OICR_ROOT and enter in this folder 
    cd $OICR_ROOT
mkdir data
cd data
  2. Download the training, validation, test data, and VOCdevkit 
    wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
  3. Extract all of these tars into one directory named VOCdevkit 
    tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar
  4. Download the VOCdevkit evaluation code adapted to octave 
    wget http://inf.ufrgs.br/~lfazeni/CVPR_deepvision2020/VOCeval_octave.tar
  5. Extract VOCeval_octave 
    tar xvf VOCeval_octave.tar
  6. Download pascal annotations in the COCO format 
    wget http://inf.ufrgs.br/~lfazeni/CVPR_deepvision2020/coco_annotations_VOC.tar
  7. Extract the annotations 
    tar xvf coco_annotations_VOC.tar
  8. It should have this basic structure 
    $VOC2007/                           
$VOC2007/annotations
$VOC2007/JPEGImages
$VOC2007/VOCdevkit        
# ... and several other directories ...
  9. [Optional] download and extract PASCAL VOC 2012. 
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar xvf VOCtrainval_11-May-2012.tar
    Observation: The 2012 test set is only available in the PASCAL VOC Evaluation Server to download. You must create a user and download it by yourself. After downloading, you can extract it in the data folder.

Extra Downloads (selective search)

  1. Download the proposals data generated by selective search to data folder 
    wget http://inf.ufrgs.br/~lfazeni/CVPR_deepvision2020/selective_search_data.tar
  2. Extract the proposals 
    tar xvf selective_search_data.tar

Extra Downloads (ImageNet models)

  1. Download the pre-trained VGG16 model to data folder 
    wget http://inf.ufrgs.br/~lfazeni/CVPR_deepvision2020/pretrained_model.tar
  2. Extract the pre-trained VGG16 model 
    tar xvf pretrained_model.tar

Usage

To Train the OICR network on VOC 2007 trainval set:

python3 code/tasks/train.py --cfg configs/baselines/vgg16_voc2007.yaml --model midn_oicr

To Evaluate the OICR network on VOC 2007:

On trainval (corloc)

 python3 code/tasks/test.py --cfg configs/baselines/vgg16_voc2007.yaml --dataset voc2007trainval --model midn_oicr --load_ckpt snapshots/midn_oicr/<some-running-date-time>/ckpt/model_step24999.pth

On test (detection mAP)

python3 code/tasks/test.py --cfg configs/baselines/vgg16_voc2007.yaml  --dataset voc2007test --model midn_oicr --load_ckpt snapshots/midn_oicr/<some-running-date-time>/ckpt/model_step24999.pth

To Visualize the detection results:

After evaluating OICR on test dataset, you will get detections.pkl. Then you can run the visualization script to show the results in a openCV window.

python3 code/tasks/visualize.py --cfg configs/baselines/vgg16_voc2007.yaml --dataset voc2007test --detections snapshots/midn_oicr/<some-running-date-time>/test/model_step24999/detections.pkl

You can also save the visualizations as images. First create a folder to save the outputs and pass it with the --output argument

mkdir output    
python3 code/tasks/visualize.py --cfg configs/baselines/vgg16_voc2007.yaml --dataset voc2007test --detections snapshots/midn_oicr/<some-running-date-time>/test/model_step24999/detections.pkl --output output

My model

You can download my model by baidu netdisk:

Link: https://pan.baidu.com/s/1BMrqbVe6uCsOgsu5rMm3mg Code: icyb

Put this folder in your $OICR_ROOT, then test and visualize model.

Note <some-running-date-time> is replaced by final in this folder, so you should also make changes when you run the code.

Notes

Below is the code structure

  • code
    • datasets: VOC dataset file
    • layers: layer and loss files
    • models: OICR model based on layers
    • tasks: train, test and visualize files
    • utils: files used for other directories
  • configs
    • baselines: config files for model and dataset
  • data
    • pretrained_model: VGG16 model weights
    • selective_search_data: selective search for VOC data
    • VOCdevikit: VOC dataset

All code files are in code directory. If you want to design a model based on OICR, you can modify layers and model.

References