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
The implementation of ICASSP 2020 paper "Pixel-level self-paced learning for super-resolution"

Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

Elon Lin 41 Dec 15, 2022
This project generates news headlines using a Long Short-Term Memory (LSTM) neural network.

News Headlines Generator bunnysaini/Generate-Headlines Goal This project aims to generate news headlines using a Long Short-Term Memory (LSTM) neural

Bunny Saini 1 Jan 24, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF

Google 625 Dec 30, 2022
🧑‍🔬 verify your TEAL program by experiment and observation

Graviton - Testing TEAL with Dry Runs Tutorial Local Installation The following instructions assume that you have make available in your local environ

Algorand 18 Jan 03, 2023
Roadmap to becoming a machine learning engineer in 2020

Roadmap to becoming a machine learning engineer in 2020, inspired by web-developer-roadmap.

Chris Hoyean Song 1.7k Dec 29, 2022
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

PocketNet This is the official repository of the paper: PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and M

Fadi Boutros 40 Dec 22, 2022
PyTorch reimplementation of REALM and ORQA

PyTorch reimplementation of REALM and ORQA

Li-Huai (Allan) Lin 17 Aug 20, 2022
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

GalaXC GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification @InProceedings{Saini21, author = {Saini, D. and Jain,

Extreme Classification 28 Dec 05, 2022
Zero-shot Learning by Generating Task-specific Adapters

Code for "Zero-shot Learning by Generating Task-specific Adapters" This is the repository containing code for "Zero-shot Learning by Generating Task-s

INK Lab @ USC 11 Dec 17, 2021
code for the ICLR'22 paper: On Robust Prefix-Tuning for Text Classification

On Robust Prefix-Tuning for Text Classification Prefix-tuning has drawed much attention as it is a parameter-efficient and modular alternative to adap

Zonghan Yang 12 Nov 30, 2022
The most simple and minimalistic navigation dashboard.

Navigation This project follows a goal to have simple and lightweight dashboard with different links. I use it to have my own self-hosted service dash

Yaroslav 23 Dec 23, 2022
Reading list for research topics in Masked Image Modeling

awesome-MIM Reading list for research topics in Masked Image Modeling(MIM). We list the most popular methods for MIM, if I missed something, please su

ligang 231 Dec 07, 2022
Code & Models for Temporal Segment Networks (TSN) in ECCV 2016

Temporal Segment Networks (TSN) We have released MMAction, a full-fledged action understanding toolbox based on PyTorch. It includes implementation fo

1.4k Jan 01, 2023
Nsdf: A mesh SDF with just some code we can directly paste into our raymarcher

nsdf Representing SDFs of arbitrary meshes has been a bit tricky so far. Express

Jan Ivanecky 5 Feb 18, 2022
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models

AdvBox 1.3k Dec 25, 2022
PyTorch implementation of adversarial patch

adversarial-patch PyTorch implementation of adversarial patch This is an implementation of the Adversarial Patch paper. Not official and likely to hav

Jamie Hayes 172 Nov 29, 2022
MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.

Introduction MASS allows you to search a time series for a subquery resulting in an array of distances. These array of distances enable you to identif

Matrix Profile Foundation 79 Dec 31, 2022
A general, feasible, and extensible framework for classification tasks.

Pytorch Classification A general, feasible and extensible framework for 2D image classification. Features Easy to configure (model, hyperparameters) T

Eugene 26 Nov 22, 2022
The code of “Similarity Reasoning and Filtration for Image-Text Matching” [AAAI2021]

SGRAF PyTorch implementation for AAAI2021 paper of “Similarity Reasoning and Filtration for Image-Text Matching”. It is built on top of the SCAN and C

Ronnie_IIAU 149 Dec 22, 2022
RefineMask (CVPR 2021)

RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features (CVPR 2021) This repo is the official implementation of RefineMask:

Gang Zhang 191 Jan 07, 2023