Multispectral Object Detection with Yolov5

Overview

Multispectral-Object-Detection

Intro

Official Code for Cross-Modality Fusion Transformer for Multispectral Object Detection.

Multispectral Object Detection with Transformer and Yolov5

Citation

If you use this repo for your research, please cite our paper:

@article{fang2021cross,
  title={Cross-Modality Fusion Transformer for Multispectral Object Detection},
  author={Fang Qingyun and Han Dapeng and Wang Zhaokui},
  journal={arXiv preprint arXiv:2111.00273},
  year={2021}
}

Installation

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7 (The same as yolov5 https://github.com/ultralytics/yolov5 ).

Clone the repo

git clone https://github.com/DocF/multispectral-object-detection

Install requirements

$ cd  multispectral-object-detection
$ pip install -r requirements.txt

Dataset

-[FLIR] download A new aligned version.

-[LLVIP] download

-[VEDAI] download

Run

Download the pretrained weights

yolov5 weights:

CFT weights:

Add the some file

create runs/train, runs/test and runs/detect three files for save the results.

Change the data cfg

some example in data/multispectral/

Train Test and Detect

train: python train.py

test: python test.py

detect: python detect_twostream.py

Results

Dataset CFT mAP50 mAP75 mAP
FLIR 73.0 32.0 37.4
FLIR ✔️ 77.7 (Δ4.7) 34.8 (Δ2.8) 40.0 (Δ2.6)
LLVIP 95.8 71.4 62.3
LLVIP ✔️ 97.5 (Δ1.7) 72.9 (Δ1.5) 63.6 (Δ1.3)
VEDAI 79.7 47.7 46.8
VEDAI ✔️ 85.3 (Δ5.6) 65.9(Δ18.2) 56.0 (Δ9.2)
Owner
Richard Fang
Richard Fang
PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021)

PyTorch code for the paper "Curriculum Graph Co-Teaching for Multi-target Domain Adaptation" (CVPR2021) This repo presents PyTorch implementation of M

Evgeny 79 Dec 19, 2022
(AAAI 2021) Progressive One-shot Human Parsing

End-to-end One-shot Human Parsing This is the official repository for our two papers: Progressive One-shot Human Parsing (AAAI 2021) End-to-end One-sh

54 Dec 30, 2022
Robust Self-augmentation for NER with Meta-reweighting

Robust Self-augmentation for NER with Meta-reweighting

Lam chi 17 Nov 22, 2022
Cowsay - A rewrite of cowsay in python

Python Cowsay A rewrite of cowsay in python. Allows for parsing of existing .cow

James Ansley 3 Jun 27, 2022
High-Fidelity Pluralistic Image Completion with Transformers (ICCV 2021)

Image Completion Transformer (ICT) Project Page | Paper (ArXiv) | Pre-trained Models | Supplemental Material This repository is the official pytorch i

Ziyu Wan 243 Jan 03, 2023
Subgraph Based Learning of Contextual Embedding

SLiCE Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks Dataset details: We use four public benchmark da

Pacific Northwest National Laboratory 27 Dec 01, 2022
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv

Zhengyang Feng 120 Dec 30, 2022
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
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Contributors of this repo: Zhibo Zhang ( Zhibo (Darren) Zhang 18 Nov 01, 2022

A collection of easy-to-use, ready-to-use, interesting deep neural network models

Interesting and reproducible research works should be conserved. This repository wraps a collection of deep neural network models into a simple and un

Aria Ghora Prabono 16 Jun 16, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
graph-theoretic framework for robust pairwise data association

CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides

MIT Aerospace Controls Laboratory 118 Dec 28, 2022
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV, 2021)

DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa

19 Oct 14, 2022
Custom Implementation of Non-Deep Networks

ParNet Custom Implementation of Non-deep Networks arXiv:2110.07641 Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun Official Repository https

Pritama Kumar Nayak 20 May 27, 2022
SBINN: Systems-biology informed neural network

SBINN: Systems-biology informed neural network The source code for the paper M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identi

Lu Group 15 Nov 19, 2022
learned_optimization: Training and evaluating learned optimizers in JAX

learned_optimization: Training and evaluating learned optimizers in JAX learned_optimization is a research codebase for training learned optimizers. I

Google 533 Dec 30, 2022
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
Code for "NeRS: Neural Reflectance Surfaces for Sparse-View 3D Reconstruction in the Wild," in NeurIPS 2021

Code for Neural Reflectance Surfaces (NeRS) [arXiv] [Project Page] [Colab Demo] [Bibtex] This repo contains the code for NeRS: Neural Reflectance Surf

Jason Y. Zhang 234 Dec 30, 2022
Fuwa-http - The http client implementation for the fuwa eco-system

Fuwa HTTP The HTTP client implementation for the fuwa eco-system Example import

Fuwa 2 Feb 16, 2022
Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021

Learning Intents behind Interactions with Knowledge Graph for Recommendation This is our PyTorch implementation for the paper: Xiang Wang, Tinglin Hua

158 Dec 15, 2022