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

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

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

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

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

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)

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

[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

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

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)
Continuous Diffusion Graph Neural Network

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Twitter Research 227 Jan 05, 2023
[内测中]前向式Python环境快捷封装工具,快速将Python打包为EXE并添加CUDA、NoAVX等支持。

QPT - Quick packaging tool 快捷封装工具 GitHub主页 | Gitee主页 QPT是一款可以“模拟”开发环境的多功能封装工具,最短只需一行命令即可将普通的Python脚本打包成EXE可执行程序,并选择性添加CUDA和NoAVX的支持,尽可能兼容更多的用户环境。 感觉还可

QPT Family 545 Dec 28, 2022
Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

SML (ICCV 2021, Oral) : Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Standardi

SangHun 61 Dec 27, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
El-Gamal on Elliptic Curve (Python)

El-Gamal-on-EC El-Gamal on Elliptic Curve (Python) References: https://docsdrive.com/pdfs/ansinet/itj/2005/299-306.pdf https://arxiv.org/ftp/arxiv/pap

3 May 04, 2022
Generic Event Boundary Detection: A Benchmark for Event Segmentation

Generic Event Boundary Detection: A Benchmark for Event Segmentation We release our data annotation & baseline codes for detecting generic event bound

47 Nov 22, 2022
We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC).

EMTAUC We provided a matlab implementation for an evolutionary multitasking AUC optimization framework (EMTAUC). In this code, SBGA is considered a ba

7 Nov 24, 2022
Codes for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”

DSAMNet The pytorch implementation for "A Deeply-supervised Attention Metric-based Network and an Open Aerial Image Dataset for Remote Sensing Change

Mengxi Liu 41 Dec 14, 2022
Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

Child-Tuning Source code for EMNLP 2021 Long paper: Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. 1. Environ

46 Dec 12, 2022
This package contains deep learning models and related scripts for RoseTTAFold

RoseTTAFold This package contains deep learning models and related scripts to run RoseTTAFold This repository is the official implementation of RoseTT

1.6k Jan 03, 2023
Code Repository for The Kaggle Book, Published by Packt Publishing

The Kaggle Book Data analysis and machine learning for competitive data science Code Repository for The Kaggle Book, Published by Packt Publishing "Lu

Packt 1.6k Jan 07, 2023
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
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

程星 87 Dec 24, 2022
Code and Resources for the Transformer Encoder Reasoning Network (TERN)

Transformer Encoder Reasoning Network Code for the cross-modal visual-linguistic retrieval method from "Transformer Reasoning Network for Image-Text M

Nicola Messina 53 Dec 30, 2022
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

CurriculumNet Introduction This repo contains related code and models from the ECCV 2018 CurriculumNet paper. CurriculumNet is a new training strategy

156 Jul 04, 2022
Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

kongdebug 14 Oct 14, 2022
Satellite labelling tool for manual labelling of storm top features such as overshooting tops, above-anvil plumes, cold U/Vs, rings etc.

Satellite labelling tool About this app A tool for manual labelling of storm top features such as overshooting tops, above-anvil plumes, cold U/Vs, ri

Czech Hydrometeorological Institute - Satellite Department 10 Sep 14, 2022
CrossMLP - The repository offers the official implementation of our BMVC 2021 paper (oral) in PyTorch.

CrossMLP Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation Bin Ren1, Hao Tang2, Nicu Sebe1. 1University of Trento, Italy, 2ETH, Switzerla

Bingoren 16 Jul 27, 2022
Scripts and outputs related to the paper Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings.

Knowledge Graph Embeddings and Chemical Effect Prediction, 2020. Scripts and outputs related to the paper Prediction of Adverse Biological Effects of

Knowledge Graphs at the Norwegian Institute for Water Research 1 Nov 01, 2021
Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch

Jiangjingwen 13 Jan 06, 2023