praeclarumjj3 / PCNetM-Experiments

Code for the experiments on the PCNet-M model.

PCNet-M Experiments on COCOA Dataset

Contents

1. Overview
2. Setup Instructions
3. Experiments

1. Overview

This repo contains the code for my experiments on mask completion using the PCNet-M model proposed in Self-Supervised Scene De-occlusion.

2. Setup Instructions

• Clone the repo:

git clone https://github.com/praeclarumjj3/PCNetM-Experiments.git
cd PCNetM-Experiments

• Install pycocotools:

pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"

• Install Pytorch and other dependencies:

pip3 install -r requirements.txt

Dataset Preparation

• Download the MS-COCO 2014 images and unzip:

wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip

• Download the annotations and untar:

gdown https://drive.google.com/uc?id=0B8e3LNo7STslZURoTzhhMFpCelE
tar -xf annotations.tar.gz

• Unzip the files according to the following structure

PCNetM-Experiments
├── data
│   ├── COCOA
│   │   ├── annotations
│   │   ├── train2014
│   │   ├── val2014

Run Demos

1. Download released models here and put the folder released under PCNetM-Experiments.

2. Run demos/demo_cocoa.ipynb. There are some test examples for demos/demo_cocoa.ipynb in the repo, so you don't have to download the COCOA dataset if you just want to try a few samples.

3. If you want to use predicted modal masks by existing instance segmentation models, you need to adjust some parameters in the demo, please refer to the answers in this issue.

3. Experiments

Training

• Run the following command:

sh experiments/COCOA/pcnet_m/train.sh # you may have to set--nproc_per_node=#YOUR_GPUS

Best Loss: 0.0674 after 44000 iterations.

Evaluate

• Execute:

sh tools/test_cocoa.sh

Results

Metric	Value
acc_allpair	0.96014
acc_occpair	0.87112
mIoU	0.81346
pAcc	0.87744

Find more results in visualizations.

Acknowledgement

This repo borrows heavily from deocclusion.