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
This repo is the official implementation of "L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization".

L2ight is a closed-loop ONN on-chip learning framework to enable scalable ONN mapping and efficient in-situ learning. L2ight adopts a three-stage learning flow that first calibrates the complicated p

Jiaqi Gu 9 Jul 14, 2022
Method for facial emotion recognition compitition of Xunfei and Datawhale .

人脸情绪识别挑战赛-第3名-W03KFgNOc-源代码、模型以及说明文档 队名:W03KFgNOc 排名:3 正确率: 0.75564 队员:yyMoming,xkwang,RichardoMu。 比赛链接:人脸情绪识别挑战赛 文章地址:link emotion 该项目分别训练八个模型并生成csv文

6 Oct 17, 2022
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
Reinforcement-learning - Repository of the class assignment questions for the course on reinforcement learning

DSE 314/614: Reinforcement Learning This repository containing reinforcement lea

Manav Mishra 4 Apr 15, 2022
This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametric Head Model (CVPR 2022)".

HeadNeRF: A Real-time NeRF-based Parametric Head Model This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametr

294 Jan 01, 2023
Pytorch implementation of One-Shot Affordance Detection

One-shot Affordance Detection PyTorch implementation of our one-shot affordance detection models. This repository contains PyTorch evaluation code, tr

46 Dec 12, 2022
The missing CMake project initializer

cmake-init - The missing CMake project initializer Opinionated CMake project initializer to generate CMake projects that are FetchContent ready, separ

1k Jan 01, 2023
A visualisation tool for Deep Reinforcement Learning

DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu

Marios Sirtmatsis 1 Nov 04, 2021
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

Wenxuan Zhou 27 Oct 28, 2022
Message Passing on Cell Complexes

CW Networks This repository contains the code used for the papers Weisfeiler and Lehman Go Cellular: CW Networks (Under review) and Weisfeiler and Leh

Twitter Research 108 Jan 05, 2023
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020)

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020) Official implementation of: Forest R-CNN: Large-Vo

Jialian Wu 54 Jan 06, 2023
nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation "

nnFormer: Interleaved Transformer for Volumetric Segmentation Code for paper "nnFormer: Interleaved Transformer for Volumetric Segmentation ". Please

jsguo 610 Dec 28, 2022
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

Please read the blog post that goes with this code! Jupyter Notebook Setup System Requirements: Python, pip (Optional) virtualenv To start the Jupyter

Denny Britz 863 Dec 15, 2022
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

Vis2Mesh This is the offical repository of the paper: Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Lear

71 Dec 25, 2022
A high-level Python library for Quantum Natural Language Processing

lambeq About lambeq is a toolkit for quantum natural language processing (QNLP). Documentation: https://cqcl.github.io/lambeq/ Getting started Prerequ

Cambridge Quantum 315 Jan 01, 2023
Configure SRX interfaces with Scrapli

Configure SRX interfaces with Scrapli Overview This example will show how to configure interfaces on Juniper's SRX firewalls. In addition to the Pytho

Calvin Remsburg 1 Jan 07, 2022
Library to enable Bayesian active learning in your research or labeling work.

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory"

memory_efficient_attention.pytorch A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory" (Rabe&Staats'21). def effic

Ryuichiro Hataya 7 Dec 26, 2022