VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

Related tags

Deep LearningVL-LTR
Overview

VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

Usage

First, install PyTorch 1.7.1+, torchvision 0.8.2+ and other required packages as follows：

conda install -c pytorch pytorch torchvision
pip install timm==0.3.2
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
pip install mmcv==1.3.14

Data preparation

ImageNet-LT

Download and extract ImageNet train and val images from here. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val/ folder respectively.

Then download and extract the wiki text into the same directory, and the directory tree of data is expected to be like this:

./data/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class2/
      img4.jpeg
  wiki/
  	desc_1.txt
  ImageNet_LT_test.txt
  ImageNet_LT_train.txt
  ImageNet_LT_val.txt
  labels.txt

After that, download the CLIP's pretrained weight RN50.pt and ViT-B-16.pt into the pretrained directory from https://github.com/openai/CLIP.

Places-LT

Download the places365_standard data from here.

Then download and extract the wiki text into the same directory. The directory tree of data is expected to be like this (almost the same as ImageNet-LT):

./data/places/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class2/
      img4.jpeg
  wiki/
  	desc_1.txt
  Places_LT_test.txt
  Places_LT_train.txt
  Places_LT_val.txt
  labels.txt

iNaturalist 2018

Download the iNaturalist 2018 data from here.

Then download and extract the wiki text into the same directory. The directory tree of data is expected to be like this:

./data/iNat/
  train_val2018/
  wiki/
  	desc_1.txt
  categories.json
  test2018.json
  train2018.json
  val.json

Evaluation

To evaluate VL-LTR with a single GPU run:

  • Pre-training stage
bash eval.sh ${CONFIG_PATH} 1 --eval-pretrain
  • Fine-tuning stage:
bash eval.sh ${CONFIG_PATH} 1

The ${CONFIG_PATH} is the relative path of the corresponding configuration file in the config directory.

Training

To train VL-LTR on a single node with 8 GPUs for:

  • Pre-training stage, run:
bash dist_train_arun.sh ${PARTITION} ${CONFIG_PATH} 8

  • Fine-tuning stage:

    • First, calculate the $\mathcal L_{\text{lin}}$ of each sentence for AnSS method by running this:
    bash eval.sh ${CONFIG_PATH} 1 --eval-pretrain --select
    • then, running this:
    bash dist_train_arun.sh ${PARTITION} ${CONFIG_PATH} 8

The ${CONFIG_PATH} is the relative path of the corresponding configuration file in the config directory.

Results

Below list our model's performance on ImageNet-LT, Places-LT, and iNaturalist 2018.

Dataset Backbone Top-1 Accuracy Download
ImageNet-LT ResNet-50 70.1 Weights
ImageNet-LT ViT-Base-16 77.2 Weights
Places-LT ResNet-50 48.0 Weights
Places-LT ViT-Base-16 50.1 Weights
iNaturalist 2018 ResNet-50 74.6 Weights
iNaturalist 2018 ViT-Base-16 76.8 Weights

For more detailed information, please refer to our paper directly.

Citation

If you are interested in our work, please cite as follows:

@article{tian2021vl,
  title={VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition},
  author={Tian, Changyao and Wang, Wenhai and Zhu, Xizhou and Wang, Xiaogang and Dai, Jifeng and Qiao, Yu},
  journal={arXiv preprint arXiv:2111.13579},
  year={2021}
}

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

You might also like...
Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022) Prerequisite PyTorch = 1.2.0 P

null 16 Dec 14, 2022
Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"

[AAAI22] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification We point out the overlooked unbiasedness in long-tailed clas

PatatiPatata 28 Oct 18, 2022
On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks

On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks We provide the code (in PyTorch) and datasets for our paper "On Size-Orient

Zemin Liu 4 Jun 18, 2022
Speech Emotion Recognition with Fusion of Acoustic- and Linguistic-Feature-Based Decisions

APSIPA-SER-with-A-and-T This code is the implementation of Speech Emotion Recognition (SER) with acoustic and linguistic features. The network model i

kenro515 3 Jan 4, 2023
A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''
A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''

README.md shall be finished soon. WSSGG 0 Overview 1 Installation 1.1 Faster-RCNN 1.2 Language Parser 1.3 GloVe Embeddings 2 Settings 2.1 VG-GT-Graph

Keren Ye 35 Nov 20, 2022
Implementation of
Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

null 105 Nov 7, 2022
Official codes for the paper
Official codes for the paper "Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech"

ResDAVEnet-VQ Official PyTorch implementation of Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech What is in this repo? M

Wei-Ning Hsu 21 Aug 23, 2022
[ICCV2021] Official code for
[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"

CTR-GCN This repo is the official implementation for Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition. The pap

Yuxin Chen 148 Dec 16, 2022
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

CASIA-IVA-Lab 67 Dec 4, 2022
Comments
  • Problem about running eval.sh

    Problem about running eval.sh

    """ #!/usr/bin/env bash set -x

    export NCCL_LL_THRESHOLD=0

    CONFIG=$1 GPUS=$1 CPUS=$[GPUS*2] PORT=${PORT:-8886}

    CONFIG_NAME=${CONFIG##/} CONFIG_NAME=${CONFIG_NAME%.}

    OUTPUT_DIR="./checkpoints/eval" if [ ! -d $OUTPUT_DIR ]; then mkdir ${OUTPUT_DIR} fi

    python -u main.py
    --port=$PORT
    --num_workers 4
    --resume "./checkpoints/${CONFIG_NAME}/checkpoint.pth"
    --output-dir ${OUTPUT_DIR}
    --config $CONFIG ${@:3}
    --eval
    2>&1 | tee -a ${OUTPUT_DIR}/train.log """ I have two A100, so set GPUS is 2. All other settings according to ReadME.md but I got a problem when running eval.sh """ File "eval.sh", line 4 export NCCL_LL_THRESHOLD=0 ^ SyntaxError: invalid syntax

    """

    opened by euminds 2
  • Mismatch between code and diagram in paper for the fine-tuning phase

    Mismatch between code and diagram in paper for the fine-tuning phase

    In fig 3, stage 2 from the paper, it looks like value for the attention is calculated based on Vision and language (Q is vision, K is language) and then applied to the language (V). But in the code, the attention is applied to the visual features. Can you verify which one is the correct way? @ChangyaoTian

    opened by rahulvigneswaran 0
  • pre-trained weights with TorchScript?

    pre-trained weights with TorchScript?

    Hello, Thanks for the great work! May I ask if it's possible for you to also provide the checkpoint weight in a TorchScript version?

    It's something like:

    import torch
import torchvision.models as models

model = models.resnet50()
traced = torch.jit.trace(model, (torch.rand(4, 3, 224, 224),))
torch.jit.save(traced, "test.pt")

# load model
model = torch.jit.load("test.pt")
    opened by xinleihe 0
Releases(ECCV-2022-video)
Owner
GitHub Repository
project page for VinVL

VinVL: Revisiting Visual Representations in Vision-Language Models Updates 02/28/2021: Project page built. Introduction This repository is the project

308 Jan 09, 2023
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

Matt Cooper 704 Nov 26, 2022
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
Code for binary and multiclass model change active learning, with spectral truncation implementation.

Model Change Active Learning Paper (To Appear) Python code for doing active learning in graph-based semi-supervised learning (GBSSL) paradigm. Impleme

Kevin Miller 1 Jul 24, 2022
MicRank is a Learning to Rank neural channel selection framework where a DNN is trained to rank microphone channels.

MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple

Samuele Cornell 20 Nov 10, 2022
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

An-Introduction-to-Statistical-Learning This repository contains the exercises and its solution contained in the book An Introduction to Statistical L

2.1k Jan 02, 2023
A Number Recognition algorithm

Paddle-VisualAttention Results_Compared SVHN Dataset Methods Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Ac

1 Nov 12, 2021
Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data.

Deep Learning Dataset Maker Deep Learning Datasets Maker is a QGIS plugin to make datasets creation easier for raster and vector data. How to use Down

deepbands 25 Dec 15, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

Pavel Izmailov 25 Sep 21, 2022
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

75 Dec 29, 2022
Certis - Certis, A High-Quality Backtesting Engine

Certis - Backtesting For y'all Certis is a powerful, lightweight, simple backtes

Yeachan-Heo 46 Oct 30, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Deepak Nandwani 1 Dec 31, 2021
Additional code for Stable-baselines3 to load and upload models from the Hub.

Hugging Face x Stable-baselines3 A library to load and upload Stable-baselines3 models from the Hub. Installation With pip Examples [Todo: add colab t

Hugging Face 34 Dec 10, 2022
CVPR2020 Counterfactual Samples Synthesizing for Robust VQA

CVPR2020 Counterfactual Samples Synthesizing for Robust VQA This repo contains code for our paper "Counterfactual Samples Synthesizing for Robust Visu

72 Dec 22, 2022
social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT Social humanoid robots with GPGPU and IoT Paper Authors Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balak

0 Jan 07, 2022
Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020 BibTeX @INPROCEEDINGS{punnappurath2020modeling, author={Abhi

Abhijith Punnappurath 22 Oct 01, 2022
Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting

Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting 1. Classification Task PyTorch implementat

Yongho Kim 0 Apr 24, 2022
PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT.

MoCo v3 for Self-supervised ResNet and ViT Introduction This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original M

Facebook Research 887 Jan 08, 2023
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023