Res2Net for Instance segmentation and Object detection using MaskRCNN

Overview

Res2Net for Instance segmentation and Object detection using MaskRCNN

Since the MaskRCNN-benchmark of facebook is deprecated, we suggest to use our mmdetection based res2net for object detection and instance segmentation to get the SOTA performance on both two tasks. https://github.com/Res2Net/mmdetection

Update

  • 2020.3.10 The mmdetection based implementation of object detection and instance segmentation using Res2Net_v1b has the SOTA performance. We have released our code on: https://github.com/Res2Net/mmdetection. Our Res2Net_v1b achieves a considerable performance gain on mmdetection compared with existing backbone models.

Introduction

This repo uses MaskRCNN as the baseline method for Instance segmentation and Object detection. We use the maskrcnn-benchmark as the baseline.

Res2Net is a powerful backbone architecture that can be easily implemented into state-of-the-art models by replacing the bottleneck with Res2Net module. More detail can be found on "Res2Net: A New Multi-scale Backbone Architecture" and our project page .

Performance

Results on Instance segmentation and Object detection using MaskRCNN.

Performance on Instance segmentation:

Backbone Setting AP AP50 AP75 APs APm APl
ResNet-50 64w 33.9 55.2 36.0 14.8 36.0 50.9
ResNet-50 48w×2s 34.2 55.6 36.3 14.9 36.8 50.9
Res2Net-50 26w×4s 35.6 57.6 37.6 15.7 37.9 53.7
Res2Net-50 18w×6s 35.7 57.5 38.1 15.4 38.1 53.7
Res2Net-50 14w×8s 35.3 57.0 37.5 15.6 37.5 53.4
ResNet-101 64w 35.5 57.0 37.9 16.0 38.2 52.9
Res2Net-101 26w×4s 37.1 59.4 39.4 16.6 40.0 55.6

Performance on Object detection:

Backbone Setting AP AP50 AP75 APs APm APl
ResNet-50 64w 37.5 58.4 40.3 20.6 40.1 49.7
ResNet-50 48w×2s 38.0 58.9 41.3 20.5 41.0 49.9
Res2Net-50 26w×4s 39.6 60.9 43.1 22.0 42.3 52.8
Res2Net-50 18w×6s 39.9 60.9 43.3 21.8 42.8 53.7
Res2Net-50 14w×8s 39.1 60.2 42.1 21.7 41.7 52.8
ResNet-101 64w 39.6 60.6 43.2 22.0 43.2 52.4
Res2Net-101 26w×4s 41.8 62.6 45.6 23.4 45.5 55.6

(Noted that pretrained models trained with pytorch usually achieve slightly worse performance than the caffe pretrained models, we took advice from the author of MaskRCNN-benchmark to use 2x schedule in all experiments including baseline and our method.)

Applications

Other applications such as Classification, Semantic segmentation, pose estimation, Class activation map can be found on https://mmcheng.net/res2net/ and https://github.com/gasvn/Res2Net .

Installation

(This repo is based on the mask-rcnn benchmark, the useage is remain the same with the original repo.)

Check INSTALL.md for installation instructions.

Perform training on COCO dataset

For the following examples to work, you need to first install maskrcnn_benchmark.

You will also need to download the COCO dataset. We recommend to symlink the path to the coco dataset to datasets/ as follows

We use minival and valminusminival sets from Detectron

# symlink the coco dataset
cd ~/github/maskrcnn-benchmark
mkdir -p datasets/coco
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2014 datasets/coco/train2014
ln -s /path_to_coco_dataset/test2014 datasets/coco/test2014
ln -s /path_to_coco_dataset/val2014 datasets/coco/val2014
# or use COCO 2017 version
ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
ln -s /path_to_coco_dataset/test2017 datasets/coco/test2017
ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017

# for pascal voc dataset:
ln -s /path_to_VOCdevkit_dir datasets/voc

P.S. COCO_2017_train = COCO_2014_train + valminusminival , COCO_2017_val = minival

You can also configure your own paths to the datasets. For that, all you need to do is to modify maskrcnn_benchmark/config/paths_catalog.py to point to the location where your dataset is stored. You can also create a new paths_catalog.py file which implements the same two classes, and pass it as a config argument PATHS_CATALOG during training.

Single GPU training

Most of the configuration files that we provide assume that we are running on 8 GPUs. In order to be able to run it on fewer GPUs, there are a few possibilities:

1. Run the following without modifications

python /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "/path/to/config/file.yaml"

This should work out of the box and is very similar to what we should do for multi-GPU training. But the drawback is that it will use much more GPU memory. The reason is that we set in the configuration files a global batch size that is divided over the number of GPUs. So if we only have a single GPU, this means that the batch size for that GPU will be 8x larger, which might lead to out-of-memory errors.

If you have a lot of memory available, this is the easiest solution.

2. Modify the cfg parameters

If you experience out-of-memory errors, you can reduce the global batch size. But this means that you'll also need to change the learning rate, the number of iterations and the learning rate schedule.

Here is an example for Mask R-CNN Res2Net-50 FPN with the 2x schedule:

python tools/train_net.py --config-file "configs/pytorch_mask_rcnn_R2_50_s4_FPN_2x.yaml" SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1

This follows the scheduling rules from Detectron. Note that we have multiplied the number of iterations by 8x (as well as the learning rate schedules), and we have divided the learning rate by 8x.

We also changed the batch size during testing, but that is generally not necessary because testing requires much less memory than training.

Multi-GPU training

We use internally torch.distributed.launch in order to launch multi-gpu training. This utility function from PyTorch spawns as many Python processes as the number of GPUs we want to use, and each Python process will only use a single GPU.

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS /path_to_maskrcnn_benchmark/tools/train_net.py --config-file "configs/pytorch_mask_rcnn_R2_50_s4_FPN_2x.yaml"

Inference in a few lines

We provide a helper class to simplify writing inference pipelines using pre-trained models. Here is how we would do it. Run this from the demo folder:

from maskrcnn_benchmark.config import cfg
from predictor import COCODemo

config_file = "../configs/pytorch_mask_rcnn_R2_50_s4_FPN_2x.yaml"

# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
cfg.merge_from_list(["MODEL.DEVICE", "cpu"])

coco_demo = COCODemo(
    cfg,
    min_image_size=800,
    confidence_threshold=0.7,
)
# load image and then run prediction
image = ...
predictions = coco_demo.run_on_opencv_image(image)

Adding your own dataset

This implementation adds support for COCO-style datasets. But adding support for training on a new dataset can be done as follows:

from maskrcnn_benchmark.structures.bounding_box import BoxList

class MyDataset(object):
    def __init__(self, ...):
        # as you would do normally

    def __getitem__(self, idx):
        # load the image as a PIL Image
        image = ...

        # load the bounding boxes as a list of list of boxes
        # in this case, for illustrative purposes, we use
        # x1, y1, x2, y2 order.
        boxes = [[0, 0, 10, 10], [10, 20, 50, 50]]
        # and labels
        labels = torch.tensor([10, 20])

        # create a BoxList from the boxes
        boxlist = BoxList(boxes, image.size, mode="xyxy")
        # add the labels to the boxlist
        boxlist.add_field("labels", labels)

        if self.transforms:
            image, boxlist = self.transforms(image, boxlist)

        # return the image, the boxlist and the idx in your dataset
        return image, boxlist, idx

    def get_img_info(self, idx):
        # get img_height and img_width. This is used if
        # we want to split the batches according to the aspect ratio
        # of the image, as it can be more efficient than loading the
        # image from disk
        return {"height": img_height, "width": img_width}

That's it. You can also add extra fields to the boxlist, such as segmentation masks (using structures.segmentation_mask.SegmentationMask), or even your own instance type.

For a full example of how the COCODataset is implemented, check maskrcnn_benchmark/data/datasets/coco.py.

Citation

If you find this work or code is helpful in your research, please cite:

@article{gao2019res2net,
  title={Res2Net: A New Multi-scale Backbone Architecture},
  author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
  journal={IEEE TPAMI},
  year={2020},
  doi={10.1109/TPAMI.2019.2938758}, 
}
@misc{massa2018mrcnn,
author = {Massa, Francisco and Girshick, Ross},
title = {{maskrnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch}},
year = {2018},
howpublished = {\url{https://github.com/facebookresearch/maskrcnn-benchmark}},
note = {Accessed: [Insert date here]}
}

Acknowledge

This code is partly borrowed from maskrcnn-benchmark. maskrcnn-benchmark is released under the MIT license. See LICENSE for additional details.

Owner
Res2Net Applications
Applications of the multi-scale backbone Res2Net (TPAMI 2020)
Res2Net Applications
LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

LV-BERT Introduction In this repo, we introduce LV-BERT by exploiting layer variety for BERT. For detailed description and experimental results, pleas

Weihao Yu 14 Aug 24, 2022
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models This repository is the official implementation of the fol

DistributedML 41 Dec 06, 2022
RobustVideoMatting and background composing in one model by using onnxruntime.

RVM_onnx_compose RobustVideoMatting and background composing in one model by using onnxruntime. Usage pip install -r requirements.txt python infer_cam

Quantum Liu 4 Apr 07, 2022
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss This repository contains the TensorFlow implementation of the paper UnF

Simon Meister 270 Nov 06, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o

Joe Ortiz 34 Dec 05, 2022
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

81 Dec 15, 2022
UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Applied Research Center (ARC), Tencent PCG 84 Jan 04, 2023
Tensorflow-seq2seq-tutorials - Dynamic seq2seq in TensorFlow, step by step

seq2seq with TensorFlow Collection of unfinished tutorials. May be good for educational purposes. 1 - simple sequence-to-sequence model with dynamic u

Matvey Ezhov 1k Dec 17, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Matthias Wright 169 Dec 26, 2022
Automatic Attendance marker for LMS Practice School Division, BITS Pilani

LMS Attendance Marker Automatic script for lazy people to mark attendance on LMS for Practice School 1. Setup Add your LMS credentials and time slot t

Nihar Bansal 3 Jun 12, 2021
Tool for installing and updating MiSTer cores and other files

MiSTer Downloader This tool installs and updates all the cores and other extra files for your MiSTer. It also updates the menu core, the MiSTer firmwa

72 Dec 24, 2022
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior. The code will release soon. Implementation Python3 PyTorch=1.0 NVIDIA GPU+

FengZhang 34 Dec 04, 2022
Leaderboard and Visualization for RLCard

RLCard Showdown This is the GUI support for the RLCard project and DouZero project. RLCard-Showdown provides evaluation and visualization tools to hel

Data Analytics Lab at Texas A&M University 246 Dec 26, 2022