OrienMask: Real-time Instance Segmentation with Discriminative Orientation Maps

Last update: Dec 13, 2022

Related tags

Deep Learning OrienMask

Overview

OrienMask

This repository implements the framework OrienMask for real-time instance segmentation.

It achieves 34.8 mask AP on COCO test-dev at the speed of 42.7 FPS evaluated with a single RTX 2080Ti. (log)

Paper: Real-time Instance Segmentation with Discriminative Orientation Maps

Installation

Please see INSTALL.md to prepare the environment and dataset.

Usage

Place the pre-trained backbone (link) and trained model (link) as follows for convenience (otherwise update the corresponding path in configurations):

├── checkpoints
│   ├── pretrained
│   │   ├──pretrained_darknet53.pth
│   ├── OrienMaskAnchor4FPNPlus
│   │   ├──orienmask_yolo.pth

train

Three items should be noticed when deploying different number of GPUs: n_gpu, batch_size, accumulate. Keep in mind that the approximate batch size equals to n_gpu * batch_size * accumulate.

# multi-gpu train (n_gpu=2, batch_size=8, accumulate=1)
# if necessary, set MASTER_PORT to avoid port conflict
# if permission error, run `chmod +x dist_train.sh`
CUDA_VISIBLE_DEVICES=0,1 ./dist_train.sh \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus

# single-gpu train (n_gpu=1, batch_size=8, accumulate=2)
CUDA_VISIBLE_DEVICES=0 ./dist_train.sh \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus
# or
CUDA_VISIBLE_DEVICES=0 python train.py \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus

test

Run the following command to obtain AP and AR metrics on val2017 split:

CUDA_VISIBLE_DEVICES=0 python test.py \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus_test \
    -w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth

infer

Please run python infer.py -h for more usages.

# infer on an image and save the visualized result
CUDA_VISIBLE_DEVICES=0 python infer.py \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus_infer \
    -w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth \
    -i assets/000000163126.jpg -v -o outputs

# infer on a list of images and save the visualized results
CUDA_VISIBLE_DEVICES=0 python infer.py \
    -c orienmask_yolo_coco_544_anchor4_fpn_plus_infer \
    -w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth \
    -d coco/test2017 -l assets/test_dev_selected.txt -v -o outputs

logs

We provide two types of logs for monitoring the training process. The first is updated on the terminal which is also stored in a train.log file in the checkpoint directory. The other is the tensorboard whose statistics are kept in the checkpoint directory.

Citation

@article{du2021realtime,
  title={Real-time Instance Segmentation with Discriminative Orientation Maps}, 
  author={Du, Wentao and Xiang, Zhiyu and Chen, Shuya and Qiao, Chengyu and Chen, Yiman and Bai, Tingming},
  journal={arXiv preprint arXiv:2106.12204},
  year={2021}
}

OrienMask: Real-time Instance Segmentation with Discriminative Orientation Maps

Related tags

Overview

OrienMask

Installation

Usage

train

test

infer

logs

Citation

Owner

[ICCV '21] In this repository you find the code to our paper Keypoint Communities

A self-supervised learning framework for audio-visual speech

PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

BC3407-Group-5-Project - BC3407 Group Project With Python

PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).

Transformer part of 12th place solution in Riiid! Answer Correctness Prediction

CaLiGraph Ontology as a Challenge for Semantic Reasoners ([email protected]'21)

Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch

Use unsupervised and supervised learning to predict stocks

Pneumonia Detection using machine learning - with PyTorch

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

Scientific Computation Methods in C and Python (Open for Hacktoberfest 2021)

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

Instance-wise Occlusion and Depth Orders in Natural Scenes (CVPR 2022)

Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

Pyeventbus: a publish/subscribe event bus

The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.