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
An implementation of the AdaOPS (Adaptive Online Packing-based Search), which is an online POMDP Solver used to solve problems defined with the POMDPs.jl generative interface.

AdaOPS An implementation of the AdaOPS (Adaptive Online Packing-guided Search), which is an online POMDP Solver used to solve problems defined with th

9 Oct 05, 2022
Machine learning Bot detection technique, based on United States election dataset

Machine learning Bot detection technique, based on United States election dataset (2020). Current github repo provides implementation described in pap

Alexander Shevtsov 4 Nov 20, 2022
Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a

4 Apr 15, 2022
Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Improved Training of Wasserstein GANs Code for reproducing experiments in "Improved Training of Wasserstein GANs". Prerequisites Python, NumPy, Tensor

Ishaan Gulrajani 2.2k Jan 01, 2023
This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

TreePartNet This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction". Depende

刘彦超 34 Nov 30, 2022
Inflated i3d network with inception backbone, weights transfered from tensorflow

I3D models transfered from Tensorflow to PyTorch This repo contains several scripts that allow to transfer the weights from the tensorflow implementat

Yana 479 Dec 08, 2022
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

SEOVER-Master This code is the implementation of paper: SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model

4 Feb 24, 2022
This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis "

kwd-extraction-study This repository is maintained for the scientific paper tittled " Study of keyword extraction techniques for Electric Double Layer

ping 543f 1 Dec 05, 2022
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"

DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is

Kevin Roitero 2 Dec 15, 2022
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps.

HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps. 中文介绍 Features Non-intrusive. Your iOS project does not need to be modi

mao2020 47 Oct 22, 2022
Hyperbolic Image Segmentation, CVPR 2022

Hyperbolic Image Segmentation, CVPR 2022 This is the implementation of paper Hyperbolic Image Segmentation (CVPR 2022). Repository structure assets :

Mina Ghadimi Atigh 46 Dec 29, 2022
General Assembly Capstone: NBA Game Predictor

Project 6: Predicting NBA Games Problem Statement Can I predict the results of NBA games from the back-half of a season from the opening half of the s

Adam Muhammad Klesc 1 Jan 14, 2022
Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

News! Aug 2020: v0.4.0 version of AlphaPose is released! Stronger tracking! Include whole body(face,hand,foot) keypoints! Colab now available. Dec 201

Machine Vision and Intelligence Group @ SJTU 6.7k Dec 28, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction This is the code for the paper Combining E

Robotics and Perception Group 69 Dec 26, 2022
An Implicit Function Theorem (IFT) optimizer for bi-level optimizations

iftopt An Implicit Function Theorem (IFT) optimizer for bi-level optimizations. Requirements Python 3.7+ PyTorch 1.x Installation $ pip install git+ht

The Money Shredder Lab 2 Dec 02, 2021
pip install python-office

🍬 python for office 👉 http://www.python4office.cn/ 👈 🌎 English Documentation 📚 简介 Python-office 是一个 Python 自动化办公第三方库,能解决大部分自动化办公的问题。而且每个功能只需一行代码,

程序员晚枫 272 Dec 29, 2022
TorchX: A PyTorch Extension Library for More Efficient Deep Learning

TorchX TorchX: A PyTorch Extension Library for More Efficient Deep Learning. @misc{torchx, author = {Ansheng You and Changxu Wang}, title = {T

Donny You 8 May 28, 2022
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

PyTorch implementation of Continuous Augmented Positional Embeddings (CAPE), by Likhomanenko et al. Enhance your Transformer positional embeddings with easy-to-use augmentations!

Guillermo Cámbara 26 Dec 13, 2022