Code for paper [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot] (ICCV 2021, oral))

Last update: May 09, 2022

Related tags

Overview

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

This repository is the official PyTorch implementation of ICCV-21 paper ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot.

Prerequirements

To install the environment.

conda env create -f environment.yml
conda activate ACE
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Data format

The annotation of a dataset is a dict consisting of two field: annotations and num_classes. The field annotations is a list of dict with image_id, fpath, im_height, im_width and category_id.

Here is an example.

{
    'annotations': [
                    {
                        'image_id': 1,
                        'fpath': '/data/iNat18/images/train_val2018/Plantae/7477/3b60c9486db1d2ee875f11a669fbde4a.jpg',
                        'im_height': 600,
                        'im_width': 800,
                        'category_id': 7477
                    },
                    ...
                   ]
    'num_classes': 8142
}

Usage

Training

#bash data_parallel_train.sh configuration_file_path GPU_indexes
bash data_parallel_train.sh configs/cifar100_im100.yaml 0,1

Testing

#python valid.py configuration_file_path
python valid.py configs/cifar100_im100.yaml

Trained models

Acknowledgement

This project is developed based on Bag of tricks @AAAI-21, thanks for their works!

Citation

@inproceedings{cai2021ace,
  title={ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot},
  author={Cai, Jiarui and Wang, Yizhou and Hwang, Jenq-Neng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={112--121},
  year={2021}
}

Code for paper [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot] (ICCV 2021, oral))

Related tags

Overview

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

Prerequirements

Data format

Usage

Training

Testing

Acknowledgement

Citation

Owner

Jiarui

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation

Deep Learning Head Pose Estimation using PyTorch.

This is an implementation of PIFuhd based on Pytorch

Deep motion generator collections

Pytorch implementation of FlowNet by Dosovitskiy et al.

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

TorchX is a library containing standard DSLs for authoring and running PyTorch related components for an E2E production ML pipeline.

Implementation of the method described in the Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory"

Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis (CVPR2022)

Like ThreeJS but for Python and based on wgpu

Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)

Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

Starter kit for getting started in the Music Demixing Challenge.

Compact Bidirectional Transformer for Image Captioning

Face Synthetics dataset is a collection of diverse synthetic face images with ground truth labels.

AbelNN: Deep Learning Python module from scratch

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

This solves the autonomous driving issue which is supported by deep learning technology. Given a video, it splits into images and predicts the angle of turning for each frame.