The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

Last update: Nov 02, 2022

Related tags

Overview

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities

This is the official code for NeurIPS 2021 Machine Learning for Autonomous Driving Workshop Paper, "Does Thermal data make the detection systems more reliable?" by Shruthi Gowda, Elahe Arani and Bahram Zonooz.

Methodology

Architecture

Detection Head : SSD
Detection Backbone : Resnet (CNN-based) or DEiT (Transformer-based)

MMC framework

MMC framework has multiple versions

KD.ENABLE: True
KD.ENABLE_DML: True

1. MMC (Base Version) : Det Loss + DML Loss 
    KD.DISTILL_TYPE : KL, AT, L2, L2B
    KL (KL divergence), AT (Attention loss), L2 (L2 norm at head layer), L2B (L2 norm of backbone features)
   
2. MMC v1 (Reconstruction) : Det Loss + DML Loss + Recon Loss
    KD.AUX_RECON = True
    KD.AUX_RECON_MODE = "normal"

3. MMC v2 (Cross Reconstruction) : Det Loss + DML Loss + Cross Recon Loss
    KD.AUX_RECON = True
    KD.AUX_RECON_MODE = "cross"

We also try other techniques for comparison

Fusion
1. Input Fusion
    KD.CONCAT_INPUT
2. Feature Fusion
    KD.CONCAT_FEATURES
    CONCAT_LAYERS

Installation

You can prepare the environment using:

pip install -r requirements.txt

You can build the project using the following script:

./build {conda_env_name}

Datasets

Two datasets "FLIR" and "KAIST" are used in this repo

FLIR : https://www.flir.eu/oem/adas/adas-dataset-form/

KAIST : https://soonminhwang.github.io/rgbt-ped-detection/

Running

Train

There are 2 networks, one receiving RGB images and one receiving thermal images. Both require different config files.

python train.py --config-file <thermal-config-file> --teacher-config-file <rgb-config-file>

Test

For evaluation only one network is used - the first network (RGB or Teacher network)

python test.py --config-file <config-file> --ckpt <model_final.pth>

Model Checkpoints

Cite Our Work

License

This project is licensed under the terms of the MIT license.

The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

Related tags

Overview

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities

Methodology

Architecture

MMC framework

Installation

Datasets

Running

Train

Test

Model Checkpoints

Cite Our Work

License

Owner

NeurAI

X-VLM: Multi-Grained Vision Language Pre-Training

PerfFuzz: Automatically Generate Pathological Inputs for C/C++ programs

MoveNetを用いたPythonでの姿勢推定のデモ

ViDT: An Efficient and Effective Fully Transformer-based Object Detector

Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

level1-image-classification-level1-recsys-09 created by GitHub Classroom

OpenDelta - An Open-Source Framework for Paramter Efficient Tuning.

MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)

Genpass - A Passwors Generator App With Python3

RoFormer_pytorch

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Contextual Attention Localization for Offline Handwritten Text Recognition

Converts geometry node attributes to built-in attributes

Sequence to Sequence Models with PyTorch

Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Transport Mode detection - can detect the mode of transport with the help of features such as acceeration,jerk etc

Official code repository for the EMNLP 2021 paper

Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.

Fully Connected DenseNet for Image Segmentation

An onlinel learning to rank python codebase.