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
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

Webis 42 Aug 14, 2022
image scene graph generation benchmark

Scene Graph Benchmark in PyTorch 1.7 This project is based on maskrcnn-benchmark Highlights Upgrad to pytorch 1.7 Multi-GPU training and inference Bat

Microsoft 303 Dec 27, 2022
This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

Soomin Park 28 Dec 16, 2022
Customised to detect objects automatically by a given model file(onnx)

LabelImg LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML

Heeone Lee 1 Jun 07, 2022
It's a powerful version of linebot

CTPS-FINAL Linbot-sever.py 主程式 Algorithm.py 推薦演算法，媒合餐廳端資料與顧客端資料 config.ini 儲存 channel-access-token、channel-secret 資料 Preface 生活在成大將近4年，我們每天的午餐時間看著形形色色

1 Oct 17, 2022
Code for "Learning to Regrasp by Learning to Place"

Learning2Regrasp Learning to Regrasp by Learning to Place, CoRL 2021. Introduction We propose a point-cloud-based system for robots to predict a seque

Shuo Cheng (成硕) 18 Aug 27, 2022
ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

ONNX-GLPDepth - Python scripts for performing monocular depth estimation using the GLPDepth model in ONNX

Ibai Gorordo 18 Nov 06, 2022
Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

Permutation Invariant Graph Generation via Score-Based Generative Modeling This repo contains the official implementation for the paper Permutation In

64 Dec 29, 2022
Complementary Patch for Weakly Supervised Semantic Segmentation, ICCV21 (poster)

CPN (ICCV2021) This is an implementation of Complementary Patch for Weakly Supervised Semantic Segmentation, which is accepted by ICCV2021 poster. Thi

Ferenas 20 Dec 12, 2022
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (Paper) (Slides) (Video) RuleBERT is a pre-trained language model that has been fine-tune

16 Aug 24, 2022
Pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model'

RTK-PAD This is an official pytorch implementation of 'Fingerprint Presentation Attack Detector Using Global-Local Model', which is accepted by IEEE T

6 Aug 01, 2022
Self-Regulated Learning for Egocentric Video Activity Anticipation

Self-Regulated Learning for Egocentric Video Activity Anticipation Introduction This is a Pytorch implementation of the model described in our paper:

qzhb 13 Sep 23, 2022
Code samples for my book "Neural Networks and Deep Learning"

Code samples for "Neural Networks and Deep Learning" This repository contains code samples for my book on "Neural Networks and Deep Learning". The cod

Michael Nielsen 13.9k Dec 26, 2022
An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit

Pengfei Liu 388 Dec 18, 2022
Repository for the semantic WMI loss

Installation: pip install -e . Installing DL2: First clone DL2 in a separate directory and install it using the following commands: git clone https:/

Nick Hoernle 4 Sep 15, 2022
PSANet: Point-wise Spatial Attention Network for Scene Parsing, ECCV2018.

PSANet: Point-wise Spatial Attention Network for Scene Parsing (in construction) by Hengshuang Zhao*, Yi Zhang*, Shu Liu, Jianping Shi, Chen Change Lo

Hengshuang Zhao 217 Oct 30, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Mail classification with tensorflow and MS Exchange Server (ham or spam).

Mail classification with tensorflow and MS Exchange Server (ham or spam).

Metin Karatas 1 Sep 11, 2021
A pre-trained model with multi-exit transformer architecture.

ElasticBERT This repository contains finetuning code and checkpoints for ElasticBERT. Towards Efficient NLP: A Standard Evaluation and A Strong Baseli

fastNLP 48 Dec 14, 2022
Learning 3D Part Assembly from a Single Image

Learning 3D Part Assembly from a Single Image This repository contains a PyTorch implementation of the paper: Learning 3D Part Assembly from A Single

18 Dec 21, 2022