Real-time Object Detection for Streaming Perception, CVPR 2022

Overview

StreamYOLO

Real-time Object Detection for Streaming Perception

Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian
Real-time Object Detection for Streaming Perception, CVPR 2022 (Oral)
Paper

Bestsoftwarechoose

Benchmark

Model size velocity sAP
0.5:0.95
sAP50 sAP75 weights COCO pretrained weights
StreamYOLO-s 600×960 1x 29.8 50.3 29.8 github github
StreamYOLO-m 600×960 1x 33.7 54.5 34.0 github github
StreamYOLO-l 600×960 1x 36.9 58.1 37.5 github github
StreamYOLO-l 600×960 2x 34.6 56.3 34.7 github github
StreamYOLO-l 600×960 still 39.4 60.0 40.2 github github

Quick Start

Dataset preparation

You can download Argoverse-1.1 full dataset and annotation from HERE and unzip it.

The folder structure should be organized as follows before our processing.

StreamYOLO
├── exps
├── tools
├── yolox
├── data
│   ├── Argoverse-1.1
│   │   ├── annotations
│   │       ├── tracking
│   │           ├── train
│   │           ├── val
│   │           ├── test
│   ├── Argoverse-HD
│   │   ├── annotations
│   │       ├── test-meta.json
│   │       ├── train.json
│   │       ├── val.json

The hash strings represent different video sequences in Argoverse, and ring_front_center is one of the sensors for that sequence. Argoverse-HD annotations correspond to images from this sensor. Information from other sensors (other ring cameras or LiDAR) is not used, but our framework can be also extended to these modalities or to a multi-modality setting.

Installation
# basic python libraries
conda create --name streamyolo python=3.7

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

pip3 install yolox==0.3
git clone [email protected]:yancie-yjr/StreamYOLO.git

cd StreamYOLO/

# add StreamYOLO to PYTHONPATH and add this line to ~/.bashrc or ~/.zshrc (change the file accordingly)
ADDPATH=$(pwd)
echo export PYTHONPATH=$PYTHONPATH:$ADDPATH >> ~/.bashrc
source ~/.bashrc

# Installing `mmcv` for the official sAP evaluation:
# Please replace `{cu_version}` and ``{torch_version}`` with the versions you are currently using.
# You will get import or runtime errors if the versions are incorrect.
pip install mmcv-full==1.1.5 -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
Reproduce our results on Argoverse-HD

Step1. Prepare COCO dataset

cd <StreamYOLO_HOME>
ln -s /path/to/your/Argoverse-1.1 ./data/Argoverse-1.1
ln -s /path/to/your/Argoverse-HD ./data/Argoverse-HD

Step2. Reproduce our results on Argoverse:

python tools/train.py -f cfgs/m_s50_onex_dfp_tal_flip.py -d 8 -b 32 -c [/path/to/your/coco_pretrained_path] -o --fp16
  • -d: number of gpu devices.
  • -b: total batch size, the recommended number for -b is num-gpu * 8.
  • --fp16: mixed precision training.
  • -c: model checkpoint path.
Offline Evaluation

We support batch testing for fast evaluation:

python tools/eval.py -f  cfgs/l_s50_onex_dfp_tal_flip.py -c [/path/to/your/model_path] -b 64 -d 8 --conf 0.01 [--fp16] [--fuse]
  • --fuse: fuse conv and bn.
  • -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
  • -b: total batch size across on all GPUs.
  • -c: model checkpoint path.
  • --conf: NMS threshold. If using 0.001, the performance will further improve by 0.2~0.3 sAP.
Online Evaluation

We modify the online evaluation from sAP

Please use 1 V100 GPU to test the performance since other GPUs with low computing power will trigger non-real-time results!!!!!!!!

cd sAP/streamyolo
bash streamyolo.sh

Citation

Please cite the following paper if this repo helps your research:

@InProceedings{streamyolo,
    author    = {Yang, Jinrong and Liu, Songtao and Li, Zeming and Li, Xiaoping and Sun, Jian},
    title     = {Real-time Object Detection for Streaming Perception},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year      = {2022}
}

License

This repo is released under the Apache 2.0 license. Please see the LICENSE file for more information.

Comments
  • when will the readme document be completed

    when will the readme document be completed

    Hi, @GOATmessi7 @yancie-yjr great wokrs. Can you enrich the readme about datasets preparing、how to training & validation and so on. hope to finish it soon. thanks

    opened by SmallMunich 1
  • ModuleNotFoundError: No module named 'exps'

    ModuleNotFoundError: No module named 'exps'

    hi everyone, I got this issue ...File "cfgs/m_s50_onex_dfp_tal_flip.py", line 189, in get_trainer from exps.train_utils.double_trainer import Trainer ModuleNotFoundError: No module named 'exps'

    Actually I ran code on local I got this error but when I try "echo export PYTHONPATH=$PYTHONPATH:$ADDPATH >> " it worked. But as you can guess my local GPU didn't enough for training. And I established everything on colab but this time "echo export..." didn't save me.

    opened by Tezcan98 3
  • A small bug in README about Dataset Prep.

    A small bug in README about Dataset Prep.

    For Developers

    Hi! When reproducing your results on Argoverse-HD, I found that the directory structure you provided in Quick Start - Dataset preparation section doesn't match the original directory structure of Argoverse-HD dataset, as well as your code required. The directory structure in Quick Start - Dataset preparation section:

    StreamYOLO
    ├── exps
    ├── tools
    ├── yolox
    ├── data
    │   ├── Argoverse-1.1
    │   │   ├── annotations
    │   │       ├── tracking
    │   │           ├── train
    │   │           ├── val
    │   │           ├── test
    │   ├── Argoverse-HD
    │   │   ├── annotations
    │   │       ├── test-meta.json
    │   │       ├── train.json
    │   │       ├── val.json
    

    should be edited as:

    StreamYOLO
    ├── exps
    ├── tools
    ├── yolox
    ├── data
    │   ├── Argoverse-1.1
    │   │   ├── tracking
    │   │       ├── train
    │   │       ├── val
    │   │       ├── test
    │   ├── Argoverse-HD
    │   │   ├── annotations
    │   │       ├── test-meta.json
    │   │       ├── train.json
    │   │       ├── val.json
    

    which matches the directory structure of the Argoverse-HD dataset: Screenshot 2022-09-21 151703.png

    For Stargazers

    BTW, if anyone manually modifies the directory structure to fit the one provided in README, an AssertionError will occur: (some parts of file path was edited)

    AssertionError: Caught AssertionError in DataLoader worker process 0.
    Original Traceback (most recent call last):
      File "%HOME%\anaconda3\envs\streamyolo\lib\site-packages\torch\utils\data\_utils\worker.py", line 198, in _worker_loop
        data = fetcher.fetch(index)
      File "%HOME%\anaconda3\envs\streamyolo\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch
        data = [self.dataset[idx] for idx in possibly_batched_index]
      File "%HOME%\anaconda3\envs\streamyolo\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
        data = [self.dataset[idx] for idx in possibly_batched_index]
      File "%HOME%\anaconda3\envs\streamyolo\lib\site-packages\yolox\data\datasets\datasets_wrapper.py", line 110, in wrapper
        ret_val = getitem_fn(self, index)
      File "%WORKSPACE%\StreamYOLO\exps\data\tal_flip_mosaicdetection.py", line 255, in __getitem__
        img, support_img, label, support_label, img_info, id_ = self._dataset.pull_item(idx)
      File "%WORKSPACE%\StreamYOLO\exps\dataset\tal_flip_one_future_argoversedataset.py", line 227, in pull_item
        img = self.load_resized_img(index)
      File "%WORKSPACE%\StreamYOLO\exps\dataset\tal_flip_one_future_argoversedataset.py", line 180, in load_resized_img
        img = self.load_image(index)
      File "%WORKSPACE%\StreamYOLO\exps\dataset\tal_flip_one_future_argoversedataset.py", line 196, in load_image
        assert img is not None
    AssertionError
    

    If anyone gets the similar error message, the content in For Developers may be helpful.

    opened by jingwenchong 6
  • Figure 2 in the paper

    Figure 2 in the paper

    Hi, I have read your paper.

    I have a question in figure 2.

    On the page3 in the paper, you wrote the expression "the output y1 of the frame F1 is matched and evaluated with the ground truth of F3 and the result of F2 is missed" about Figure 2.

    I understood like that expression mean y1 is the output of the none-real-time detectors of frame F1.

    But, before the frame F3 is received, the frame F2 is received in first.

    So I can't understand that point and I also want to ask when the output of the frame f0 come out.

    opened by wpdlatm1452 1
  • How can i save the detection result?

    How can i save the detection result?

    Hi, thank you for suggesting your nice code.

    I trained the model using Argoverse dataset following your readme.

    I want to run demo and save detection results (image or video), how can i do that?

    thank you.

    opened by daminlee1 0
Owner
Jinrong Yang
Research: Object detection, Deep learning
Jinrong Yang
Python parser for DTED data.

DTED Parser This is a package written in pure python (with help from numpy) to parse and investigate Digital Terrain Elevation Data (DTED) files. This

Ben Bonenfant 12 Dec 18, 2022
Evolution Strategies in PyTorch

Evolution Strategies This is a PyTorch implementation of Evolution Strategies. Requirements Python 3.5, PyTorch = 0.2.0, numpy, gym, universe, cv2 Wh

Andrew Gambardella 333 Nov 14, 2022
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 03, 2022
PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L

BigDyL 7 Dec 27, 2022
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

3k Jan 08, 2023
Code for the paper "Unsupervised Contrastive Learning of Sound Event Representations", ICASSP 2021.

Unsupervised Contrastive Learning of Sound Event Representations This repository contains the code for the following paper. If you use this code or pa

Eduardo Fonseca 81 Dec 22, 2022
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language (NeurIPS 2021)

VRDP (NeurIPS 2021) Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language Mingyu Ding, Zhenfang Chen, Tao Du, Pin

Mingyu Ding 36 Sep 20, 2022
A library for uncertainty representation and training in neural networks.

Epistemic Neural Networks A library for uncertainty representation and training in neural networks. Introduction Many applications in deep learning re

DeepMind 211 Dec 12, 2022
A python code to convert Keras pre-trained weights to Pytorch version

Weights_Keras_2_Pytorch 最近想在Pytorch项目里使用一下谷歌的NIMA,但是发现没有预训练好的pytorch权重,于是整理了一下将Keras预训练权重转为Pytorch的代码,目前是支持Keras的Conv2D, Dense, DepthwiseConv2D, Batch

Liu Hengyu 2 Dec 16, 2021
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
ML model to classify between cats and dogs

Cats-and-dogs-classifier This is my first ML model which can classify between cats and dogs. Here the accuracy is around 75%, however , the accuracy c

Sharath V 4 Aug 20, 2021
ObsPy: A Python Toolbox for seismology/seismological observatories.

ObsPy is an open-source project dedicated to provide a Python framework for processing seismological data. It provides parsers for common file formats

ObsPy 979 Jan 07, 2023
Repository sharing code and the model for the paper "Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes"

Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes Setup virtualenv -p python3 venv source venv/bin/activate pip instal

Planet AI GmbH 9 May 20, 2022
API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API

RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes

20 Jan 05, 2023
This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".

On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness This repository provides the code for the paper On Interaction B

Meta Research 33 Dec 08, 2022
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Tensorflow code and models for the paper: Large Scale Fine-Grained Categ

Yin Cui 187 Oct 01, 2022
Back to Basics: Efficient Network Compression via IMP

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

IOL Lab @ ZIB 1 Nov 19, 2021
Official code for UnICORNN (ICML 2021)

UnICORNN (Undamped Independent Controlled Oscillatory RNN) [ICML 2021] This repository contains the implementation to reproduce the numerical experime

Konstantin Rusch 21 Dec 22, 2022
Code for "Learning Canonical Representations for Scene Graph to Image Generation", Herzig & Bar et al., ECCV2020

Learning Canonical Representations for Scene Graph to Image Generation (ECCV 2020) Roei Herzig*, Amir Bar*, Huijuan Xu, Gal Chechik, Trevor Darrell, A

roei_herzig 24 Jul 07, 2022
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

3 Dec 26, 2022