FairMOT for Multi-Class MOT using YOLOX as Detector

Overview

FairMOT-X

Project Overview

FairMOT-X is a multi-class multi object tracker, which has been tailored for training on the BDD100K MOT Dataset. It makes use of YOLOX as the detector from end-to-end, and uses DCN to perform feature fusion of PAFPN outputs to learn the ReID branch. This repo is a work in progress.

Acknowledgement

This project heavily uses code from the the original FairMOT, as well as MCMOT and YOLOv4 MCMOT.

Comments
  • Detailed readme

    Detailed readme

    Thanks for your excellent work!And i have the same idea with you but i can't implement it,Can you provide detailed insatallation in reame file or your contact information,that's a milestone in my research. Thank you in advance!

    opened by Soyad-yao 10
  • how to train on other datasets

    how to train on other datasets

    Hello ! First,thank you for your work! But I have a question. I want to train on other datasets not bdd100k , such as detrac, how to use? Thank you very much!

    opened by fafa114 2
  • Conda environment

    Conda environment

    Could you please share a working environment requirements list for this repo? I followed FairMOT installation procedure but I am unable to start a sample training. I got the following error:

    python3 ./src/train.py mot \

    --exp_id yolo-m --yolo_depth 0.67 --yolo_width 0.75 \
    --lr 7e-4 --lr_step 2 \
    --reid_dim 128 --augment --mosaic \
    --batch_size 16 --gpus 0 
    

    /home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/image.so: undefined symbol: _ZNK3c106IValue23reportToTensorTypeErrorEv warn(f"Failed to load image Python extension: {e}") Traceback (most recent call last): File "./src/train.py", line 16, in from torchvision.transforms import transforms as T File "/home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/init.py", line 7, in from torchvision import models File "/home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/models/init.py", line 18, in from . import quantization File "/home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/models/quantization/init.py", line 3, in from .mobilenet import * File "/home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/models/quantization/mobilenet.py", line 1, in from .mobilenetv2 import * # noqa: F401, F403 File "/home/fatih/miniconda3/envs/fairmot-x/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv2.py", line 6, in from torch.ao.quantization import QuantStub, DeQuantStub ModuleNotFoundError: No module named 'torch.ao'

    opened by youonlytrackonce 0
  • How to get the tracking indicators, such as Mota

    How to get the tracking indicators, such as Mota

    I want to know how to get the tracking indicators, such as MOTA, only "python3 track.py"? But when I run track.py ,always show "[Warning]: No objects detected." I don't know why. And I can't get indicators . But I can get images after tracking on BDD100k MOT dataset.

    opened by fafa114 0
  • train log

    train log

    Thanks for your work! I follow your code and then implement yolox+fairmot in mmdetection. But the ReID loss does not descend. Would you mind uploading your train log as a reference ?

    opened by taofuyu 3
Releases(Weights)
Owner
Jonathan Tan
Mech. Engineering Undergraduate at NUS with deep interest in machine learning and robotics.
Jonathan Tan
DAT4 - General Assembly's Data Science course in Washington, DC

DAT4 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (12/15/14 - 3/16/15). Instructors: Sinan Ozdemir

Kevin Markham 779 Dec 25, 2022
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
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Link to the paper: https://arxiv.org/pdf/2111.14271.pdf Contributors of this repo: Zhibo Zha

Zhibo (Darren) Zhang 18 Nov 01, 2022
CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

CS_Final_Metal_surface_detection This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021. The project is based on the dataset

Cuong Vo 1 Dec 29, 2021
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors This package provides a simulator for vision-based

Facebook Research 255 Dec 27, 2022
ADSPM: Attribute-Driven Spontaneous Motion in Unpaired Image Translation

ADSPM: Attribute-Driven Spontaneous Motion in Unpaired Image Translation This repository provides a PyTorch implementation of ADSPM. Requirements Pyth

24 Jul 24, 2022
Auto grind btdb2 exp for tower

Bloons TD Battles 2 EXP Grinder Auto grind btdb2 exp for towers Setup I suggest checking out every screenshot to see what they are supposed to be, so

Vincent 6 Jul 29, 2022
Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network

Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Network This is the official implementation of

azad 2 Jul 09, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Sohil Shah 197 Nov 29, 2022
Robot Reinforcement Learning on the Constraint Manifold

Implementation of "Robot Reinforcement Learning on the Constraint Manifold"

31 Dec 05, 2022
Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

PyGAS: Auto-Scaling GNNs in PyG PyGAS is the practical realization of our G NN A uto S cale (GAS) framework, which scales arbitrary message-passing GN

Matthias Fey 139 Dec 25, 2022
A simple, high level, easy-to-use open source Computer Vision library for Python.

ZoomVision : Slicing Aid Detection A simple, high level, easy-to-use open source Computer Vision library for Python. Installation Installing dependenc

Nurettin Sinanoğlu 2 Mar 04, 2022
Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Noah Getz 3 Jun 22, 2022
A model which classifies reviews as positive or negative.

SentiMent Analysis In this project I built a model to classify movie reviews fromn the IMDB dataset of 50K reviews. WordtoVec : Neural networks only w

Rishabh Bali 2 Feb 09, 2022
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [toc] 1. Introduction This repository provides the code for our paper at

13 Dec 08, 2022
Image Classification - A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

0 Jan 23, 2022
Unadversarial Examples: Designing Objects for Robust Vision

Unadversarial Examples: Designing Objects for Robust Vision This repository contains the code necessary to replicate the major results of our paper: U

Microsoft 93 Nov 28, 2022
NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

NCVX NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning. Please check https://ncvx.org for detailed instruction

SUN Group @ UMN 28 Aug 03, 2022