3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A - Continual Learning Classification Challenge

Overview

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay

3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A - Continual Learning Classification

Technical Report slides
video

Description

Official implementation of our solution (3rd place) for ICCV 2021 Workshop Self-supervised Learning for Next-Generation Industry-level Autonomous Driving (SSLAD) Track 3A - Continual Learning Classification using "Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay".

How to run

First, install dependencies

# clone project   
git clone https://github.com/mrifkikurniawan/sslad.git

# install project   
cd sslad 
pip3 install -r requirements.txt   

Next, preparing the dataset via links below.

Next, run training.

# run training module with our proposed cl strategy
python3.9 classification.py \
--config configs/cl_strategy.yaml \
--name {path/to/log} \
--root {root/of/your/dataset} \
--num_workers {num workers} \
--gpu_id {your-gpu-id} \
--comment {any-comments} 
--store \

or see the train.sh for the example.

Experiments Results

Method Val AMCA Test AMCA
Baseline (Uncertainty Replay)* 57.517 -
+ Multi-step Lr Scheduler* 59.591 (+2.07) -
+ Soft Labels Retrospection* 59.825 (+0.23) -
+ Contrastive Learning* 60.363 (+0.53) 59.68
+ Supervised Contrastive Learning* 61.49 (+1.13) -
+ Change backbone to ResNet50-D* 62.514 (+1.02) -
+ Focal loss* 62.71 (+0.19) -
+ Cost Sensitive Cross Entropy 63.33 (+0.62) -
+ Class Balanced Focal loss* 64.01 (+1.03) 64.53 (+4.85)
+ Head Fine-tuning with Class Balanced Replay 65.291 (+1.28) 62.58 (-1.56)
+ Head Fine-tuning with Soft Labels Retrospection 66.116 (+0.83) 62.97 (+0.39)

*Applied to our final method.

File overview

classification.py: Driver code for the classification subtrack. There are a few things that can be changed here, such as the model, optimizer and loss criterion. There are several arguments that can be set to store results etc. (Run classification.py --help to get an overview, or check the file.)

class_strategy.py: Provides an empty plugin. Here, you can define your own strategy, by implementing the necessary callbacks. Helper methods and classes can be ofcourse implemented as pleased. See here for examples of strategy plugins.

data_intro.ipynb: In this notebook the stream of data is further introduced and explained. Feel free to experiment with the dataset to get a good feeling of the challenge.

Note: not all callbacks have to be implemented, you can just delete those that you don't need.

classification_util.py & haitain_classification.py: These files contain helper code for dataloading etc. There should be no reason to change these.

Owner
Rifki Kurniawan
MS student at Xi'an Jiaotong University; Artificial Intelligence Engineer at Nodeflux
Rifki Kurniawan
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
Python implementation of Wu et al (2018)'s registration fusion

reg-fusion Projection of a central sulcus probability map using the RF-ANTs approach (right hemisphere shown). This is a Python implementation of Wu e

Dan Gale 26 Nov 12, 2021
Reinforcement learning algorithms in RLlib

raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b

Ângelo 50 Sep 08, 2022
A solution to the 2D Ising model of ferromagnetism, implemented using the Metropolis algorithm

Solving the Ising model on a 2D lattice using the Metropolis Algorithm Introduction The Ising model is a simplified model of ferromagnetism, the pheno

Rohit Prabhu 5 Nov 13, 2022
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
How the Deep Q-learning method works and discuss the new ideas that makes the algorithm work

Deep Q-Learning Recommend papers The first step is to read and understand the method that you will implement. It was first introduced in a 2013 paper

1 Jan 25, 2022
Wordplay, an artificial Intelligence based crossword puzzle solver.

Wordplay, AI based crossword puzzle solver A crossword is a word puzzle that usually takes the form of a square or a rectangular grid of white- and bl

Vaibhaw 4 Nov 16, 2022
Unofficial PyTorch Implementation of Multi-Singer

Multi-Singer Unofficial PyTorch Implementation of Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus. Requirements See re

SunMail-hub 123 Dec 28, 2022
Code for Paper: Self-supervised Learning of Motion Capture

Self-supervised Learning of Motion Capture This is code for the paper: Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki, Self-sup

Hsiao-Yu Fish Tung 87 Jul 25, 2022
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representa

Bats Research 94 Nov 21, 2022
An abstraction layer for mathematical optimization solvers.

MathOptInterface Documentation Build Status Social An abstraction layer for mathematical optimization solvers. Replaces MathProgBase. Citing MathOptIn

JuMP-dev 284 Jan 04, 2023
Utilities to bridge Canvas-generated course rosters with GitLab's API.

gitlab-canvas-utils A collection of scripts originally written for CSE 13S. Oversees everything from GitLab course group creation, student repository

Eugene Chou 5 Jun 08, 2022
A modular PyTorch library for optical flow estimation using neural networks

A modular PyTorch library for optical flow estimation using neural networks

neu-vig 113 Dec 20, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
Codebase for the Summary Loop paper at ACL2020

Summary Loop This repository contains the code for ACL2020 paper: The Summary Loop: Learning to Write Abstractive Summaries Without Examples. Training

Canny Lab @ The University of California, Berkeley 44 Nov 04, 2022
Dilated RNNs in pytorch

PyTorch Dilated Recurrent Neural Networks PyTorch implementation of Dilated Recurrent Neural Networks (DilatedRNN). Getting Started Installation: $ pi

Zalando Research 200 Nov 17, 2022
The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also pr

Meta Research 1 Dec 02, 2021
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

This is a playground for pytorch beginners, which contains predefined models on popular dataset. Currently we support mnist, svhn cifar10, cifar100 st

Aaron Chen 2.4k Dec 28, 2022
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

CLOCs is a novel Camera-LiDAR Object Candidates fusion network. It provides a low-complexity multi-modal fusion framework that improves the performance of single-modality detectors. CLOCs operates on

Su Pang 254 Dec 16, 2022
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Documentation | FAQ | Release Notes | Roadmap | MACE Model Zoo | Demo | Join Us | 中文 Mobile AI Compute Engine (or MACE for short) is a deep learning i

Xiaomi 4.7k Dec 29, 2022