ML-Decoder: Scalable and Versatile Classification Head

Overview

ML-Decoder: Scalable and Versatile Classification Head

PWC
PWC
PWC


Paper

Official PyTorch Implementation

Tal Ridnik, Gilad Sharir, Avi Ben-Cohen, Emanuel Ben-Baruch, Asaf Noy
DAMO Academy, Alibaba Group

Abstract

In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the decoder architecture, and using a novel group-decoding scheme, ML-Decoder is highly efficient, and can scale well to thousands of classes. Compared to using a larger backbone, ML-Decoder consistently provides a better speed-accuracy trade-off. ML-Decoder is also versatile - it can be used as a drop-in replacement for various classification heads, and generalize to unseen classes when operated with word queries. Novel query augmentations further improve its generalization ability. Using ML-Decoder, we achieve state-of-the-art results on several classification tasks: on MS-COCO multi-label, we reach 91.4% mAP; on NUS-WIDE zero-shot, we reach 31.1% ZSL mAP; and on ImageNet single-label, we reach with vanilla ResNet50 backbone a new top score of 80.7%, without extra data or distillation.

ML-Decoder Implementation

ML-Decoder implementation is available here. It can be easily integrated into any backbone using this example code:

ml_decoder_head = MLDecoder(num_classes) # initilization

spatial_embeddings = self.backbone(input_image) # backbone generates spatial embeddings      
 
logits = ml_decoder_head(spatial_embeddings) # transfrom spatial embeddings to logits

Training Code

We will share a full reproduction code for the article results.

Multi-label Training Code


A reproduction code for MS-COCO multi-label:

python train.py  \
--data=/home/datasets/coco2014/ \
--model_name=tresnet_l \
--image_size=448

Single-label Training Code

Our single-label training code uses the excellent timm repo. Reproduction code is currently from a fork, we will work toward a full merge to the main repo.

git clone https://github.com/mrT23/pytorch-image-models.git

This is the code for A2 configuration training, with ML-Decoder (--use-ml-decoder-head=1):

python -u -m torch.distributed.launch --nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
./train.py \
/data/imagenet/ \
--amp \
-b=256 \
--epochs=300 \
--drop-path=0.05 \
--opt=lamb \
--weight-decay=0.02 \
--sched='cosine' \
--lr=4e-3 \
--warmup-epochs=5 \
--model=resnet50 \
--aa=rand-m7-mstd0.5-inc1 \
--reprob=0.0 \
--remode='pixel' \
--mixup=0.1 \
--cutmix=1.0 \
--aug-repeats 3 \
--bce-target-thresh 0.2 \
--smoothing=0 \
--bce-loss \
--train-interpolation=bicubic \
--use-ml-decoder-head=1

ZSL Training Code

Reproduction code for ZSL is WIP.

Citation

@misc{ridnik2021mldecoder,
      title={ML-Decoder: Scalable and Versatile Classification Head}, 
      author={Tal Ridnik and Gilad Sharir and Avi Ben-Cohen and Emanuel Ben-Baruch and Asaf Noy},
      year={2021},
      eprint={2111.12933},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields

MINE: Continuous-Depth MPI with Neural Radiance Fields Project Page | Video PyTorch implementation for our ICCV 2021 paper. MINE: Towards Continuous D

Zijian Feng 325 Dec 29, 2022
4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR) at PBVS2022

A Two-Stage Shake-Shake Network for Long-tailed Recognition of SAR Aerial View Objects 4st place solution for the PBVS 2022 Multi-modal Aerial View Ob

LinpengPan 5 Nov 09, 2022
Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

IterMVS official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo' Introduction IterMVS is a novel lear

Fangjinhua Wang 127 Jan 04, 2023
Exploration & Research into cross-domain MEV. Initial focus on ETH/POLYGON.

xMEV, an apt exploration This is a small exploration on the xMEV opportunities between Polygon and Ethereum. It's a data analysis exercise on a few pa

odyslam.eth 7 Oct 18, 2022
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
Python interface for the DIGIT tactile sensor

DIGIT-INTERFACE Python interface for the DIGIT tactile sensor. For updates and discussions please join the #DIGIT channel at the www.touch-sensing.org

Facebook Research 35 Dec 22, 2022
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022
Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"

Subg-Con Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning (Jiao et al., ICDM 2020): https://arxiv.org/abs/2009.10273 Over

34 Jul 06, 2022
Combine Tacotron2 and Hifi GAN to generate speech from text

EndToEndTextToSpeech Combine Tacotron2 and Hifi GAN to generate speech from text Download weights Hifi GAN - hifi_gan/checkpoint/ : pretrain 2.5M ste

Phạm Quốc Huy 1 Dec 18, 2021
Tutorial repo for an end-to-end Data Science project

End-to-end Data Science project This is the repo with the notebooks, code, and additional material used in the ITI's workshop. The goal of the session

Deena Gergis 127 Dec 30, 2022
TensorFlow implementation of "Variational Inference with Normalizing Flows"

[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co

YeongHyeon Park 7 Jun 08, 2022
Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

SNN_Calibration Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021 Feature Comparison of SNN calibration: Features SNN Direct Tr

Yuhang Li 60 Dec 27, 2022
Neural Ensemble Search for Performant and Calibrated Predictions

Neural Ensemble Search Introduction This repo contains the code accompanying the paper: Neural Ensemble Search for Performant and Calibrated Predictio

AutoML-Freiburg-Hannover 26 Dec 12, 2022
Lua-parser-lark - An out-of-box Lua parser written in Lark

An out-of-box Lua parser written in Lark Such parser handles a relaxed version o

Taine Zhao 2 Jul 19, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching(CVPR2021)

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

106 Dec 28, 2022
Dynamical Wasserstein Barycenters for Time Series Modeling

Dynamical Wasserstein Barycenters for Time Series Modeling This is the code related for the Dynamical Wasserstein Barycenter model published in Neurip

8 Sep 09, 2022
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning

HiEST 2 Sep 09, 2022
duralava is a neural network which can simulate a lava lamp in an infinite loop.

duralava duralava is a neural network which can simulate a lava lamp in an infinite loop. Example This is not a real lava lamp but a "fake" one genera

Maximilian Bachl 87 Dec 20, 2022
Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis

Hierarchical Attention Mining (HAM) for weakly-supervised abnormality localization This is the official PyTorch implementation for the HAM method. Pap

Xi Ouyang 22 Jan 02, 2023