CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

Overview

CV Backbones

including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab.

News

2022/01/05 PyramidTNT: An improved TNT baseline is released.

2021/09/28 The paper of TNT (Transformer in Transformer) is accepted by NeurIPS 2021.

2021/09/18 The extended version of Versatile Filters is accepted by T-PAMI.

2021/08/30 GhostNet paper is selected as the Most Influential CVPR 2020 Papers.

2021/08/26 The codes of LegoNet and Versatile Filters has been merged into this repo.

2021/06/15 The code of TNT (Transformer in Transformer) has been released in this repo.

2020/10/31 GhostNet+TinyNet achieves better performance. See details in our NeurIPS 2020 paper: arXiv.

2020/06/10 GhostNet is included in PyTorch Hub.


GhostNet Code

This repo provides GhostNet pretrained models and inference code for TensorFlow and PyTorch:

For training, please refer to tinynet or timm.

TinyNet Code

This repo provides TinyNet pretrained models and inference code for PyTorch:

TNT Code

This repo provides training code and pretrained models of TNT (Transformer in Transformer) for PyTorch:

The code of PyramidTNT is also released:

LegoNet Code

This repo provides the implementation of paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ICML 2019)

Versatile Filters Code

This repo provides the implementation of paper Learning Versatile Filters for Efficient Convolutional Neural Networks (NeurIPS 2018)

Citation

@inproceedings{ghostnet,
  title={GhostNet: More Features from Cheap Operations},
  author={Han, Kai and Wang, Yunhe and Tian, Qi and Guo, Jianyuan and Xu, Chunjing and Xu, Chang},
  booktitle={CVPR},
  year={2020}
}
@inproceedings{tinynet,
  title={Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets},
  author={Han, Kai and Wang, Yunhe and Zhang, Qiulin and Zhang, Wei and Xu, Chunjing and Zhang, Tong},
  booktitle={NeurIPS},
  year={2020}
}
@inproceedings{tnt,
  title={Transformer in transformer},
  author={Han, Kai and Xiao, An and Wu, Enhua and Guo, Jianyuan and Xu, Chunjing and Wang, Yunhe},
  booktitle={NeurIPS},
  year={2021}
}
@inproceedings{legonet,
    title={LegoNet: Efficient Convolutional Neural Networks with Lego Filters},
    author={Yang, Zhaohui and Wang, Yunhe and Liu, Chuanjian and Chen, Hanting and Xu, Chunjing and Shi, Boxin and Xu, Chao and Xu, Chang},
    booktitle={ICML},
    year={2019}
  }
@inproceedings{wang2018learning,
  title={Learning versatile filters for efficient convolutional neural networks},
  author={Wang, Yunhe and Xu, Chang and Chunjing, XU and Xu, Chao and Tao, Dacheng},
  booktitle={NeurIPS},
  year={2018}
}

Other versions of GhostNet

This repo provides the TensorFlow/PyTorch code of GhostNet. Other versions and applications can be found in the following:

  1. timm: code with pretrained model
  2. Darknet: cfg file, and description
  3. Gluon/Keras/Chainer: code
  4. Paddle: code
  5. Bolt inference framework: benckmark
  6. Human pose estimation: code
  7. YOLO with GhostNet backbone: code
  8. Face recognition: cavaface, FaceX-Zoo, TFace
Comments
  • TypeError: __init__() got an unexpected keyword argument 'bn_tf'

    TypeError: __init__() got an unexpected keyword argument 'bn_tf'

    Hello, I want to ask what caused the following error when running the train.py file? thank you “ TypeError: init() got an unexpected keyword argument 'bn_tf' ”

    opened by ModeSky 16
  • Counting ReLU vs HardSwish FLOPs

    Counting ReLU vs HardSwish FLOPs

    Thank you very much for sharing the source code. I have a question related to FLOPs counting for ReLU and HardSwish. I saw in the paper the flops are the same in ReLU and HardSwish. Can you explain this situation? image

    opened by jahongir7174 10
  • kernel size in primary convolution of Ghost module

    kernel size in primary convolution of Ghost module

    Hi, It is said in your paper that the primary convolution in Ghost module can have customized kernel size, which is a major difference from existing efficient convolution schemes. However, it seems that in this code all the kernel size of primary convolution in Ghost module are set to [1, 1], and the kernel set in _CONV_DEFS_0 are only used in blocks of stride=2. Is it set intentionally?

    opened by YUHAN666 9
  • 用GhostModule替换Conv2d,loss降的很慢?

    用GhostModule替换Conv2d,loss降的很慢?

    我直接将efficientnet里面的MBConvBlock中的Conv2d替换为GhostModule: Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False) 替换为 GhostModule(inp, oup), 其他参数不变,为什么损失比以前收敛的更慢了,一直降不下来?请问需要修改其他什么参数吗?

    opened by yc-cui 8
  • Training hyperparams on ImageNet

    Training hyperparams on ImageNet

    Hi, thanks for sharing such a wonderful work, I'd like to reproduce your results on ImageNet, could you please specify training parameters such as initial learning rate, how to decay it, batch size, etc. It would be even better if you can provide tricks to train GhostNet, such as label smoothing and data augmentation. Thx!

    good first issue 
    opened by sean-zhuh 8
  • Why did you exclude EfficientNetB0 from Accuracy-Latency chart?

    Why did you exclude EfficientNetB0 from Accuracy-Latency chart?

    @iamhankai Hi,

    Great work!

    1. Why did you exclude EfficientNetB0 (0.390 BFlops - 76.3% Top1) from Accuracy-Latency chart?

    2. Also what mini_batch_size did you use for training GhostNet?

    flops_latency

    opened by AlexeyAB 8
  • VIG pretrained weights

    VIG pretrained weights

    @huawei-noah-admin cna you please share the VIG pretraiend model on google drive or one drive as baidu is not accessible from our end

    THank in advance

    opened by abhigoku10 7
  • The implementation of Isotropic architecture

    The implementation of Isotropic architecture

    Hi, thanks for sharing this impressive work. The paper mentioned two architectures, Isotropic one and pyramid one. I noticed that in the code, this is a reduce_ratios, and this reduce_ratios are used by a avg_pooling operation to calculate before building the graph. I am wondering whether all I need to do is setting this reduce_ratios to [1,1,1,1] if I want to implement the Isotropic architecture. Thanks.

    self.n_blocks = sum(blocks) channels = opt.channels reduce_ratios = [4, 2, 1, 1] dpr = [x.item() for x in torch.linspace(0, drop_path, self.n_blocks)] num_knn = [int(x.item()) for x in torch.linspace(k, k, self.n_blocks)]

    opened by buptxiaofeng 6
  • Gradient overflow occurs while training tnt-ti model

    Gradient overflow occurs while training tnt-ti model

    ^@^@Train: 41 [ 0/625 ( 0%)] Loss: 4.564162 (4.5642) Time: 96.744s, 21.17/s (96.744s, 21.17/s) LR: 8.284e-04 Data: 94.025 (94.025) ^@^@^@^@Train: 41 [ 50/625 ( 8%)] Loss: 4.395192 (4.4797) Time: 2.742s, 746.96/s (7.383s, 277.38/s) LR: 8.284e-04 Data: 0.057 (4.683) ^@^@^@^@Train: 41 [ 100/625 ( 16%)] Loss: 4.424296 (4.4612) Time: 2.741s, 747.15/s (6.529s, 313.66/s) LR: 8.284e-04 Data: 0.056 (3.831) Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0 Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0 Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0 Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0

    And the top-1 acc is only 0.2 after 40 epochs.

    Any tips available here, dear @iamhankai @yitongh

    opened by jimmyflycv 6
  • Bloated model

    Bloated model

    Hi, I am using Ghostnet backbone for training YoloV3 model in Tensorflow, but I am getting a bloated model. The checkpoint data size is approx. 68MB, but the checkpoint given here is of approx 20MB https://github.com/huawei-noah/ghostnet/blob/master/tensorflow/models/ghostnet_checkpoint.data-00000-of-00001

    I am also training EfficientNet model with YoloV3 and that seems to be working fine, without any bloated size.

    Could anyone or the author please confirm if this is the correct architecture or anything seems weird? I have attached the Ghostnet architecture file out of the code.

    Thanks. ghostnet_model_arch.txt

    opened by ghost 6
  • Replace Conv2d in my network, however it becomes slower, why?

    Replace Conv2d in my network, however it becomes slower, why?

    Above all, thanks for your great work! It really inspires me a lot! But now I have a question.

    I replace all the Conv2d operations in my network except the final ones, the model parameters really becomes much more less. However, when testing, I found that the average forward time decreases a lot by the replacement (from 428FPS down to 354FPS). So, is this a normal phenomenon? Or is this because of the concat operation?

    opened by FunkyKoki 6
  • VIG for segmenation

    VIG for segmenation

    @iamhankai thanks for open-sourcing the code base . Can you please let me knw how to use the pvig for segmentation related activities its really helpful

    THanks in advance

    opened by abhigoku10 0
  • higher performance of ViG

    higher performance of ViG

    I try to train ViG-S on ImageNet and get 80.54% top1 accuracy, which is higher than that in paper, 80.4%. I wonder if 80.4 is the average of multiple trainings? If yes, how many reps do you use?

    opened by tdzdog 9
Releases(GhostNetV2)
Owner
HUAWEI Noah's Ark Lab
Working with and contributing to the open source community in data mining, artificial intelligence, and related fields.
HUAWEI Noah's Ark Lab
[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding Official Pytorch implementation of Negative Sample Matter

Multimedia Computing Group, Nanjing University 69 Dec 26, 2022
Code, pre-trained models and saliency results for the paper "Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images".

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB This repository is the official implementation of the paper. Our results comming soon in

Xiaoqiang Wang 8 May 22, 2022
Build a medical knowledge graph based on Unified Language Medical System (UMLS)

UMLS-Graph Build a medical knowledge graph based on Unified Language Medical System (UMLS) Requisite Install MySQL Server 5.6 and import UMLS data int

Donghua Chen 6 Dec 25, 2022
A parallel framework for population-based multi-agent reinforcement learning.

MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested

MARL @ SJTU 348 Jan 08, 2023
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset

CPPE - 5 CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization

Rishit Dagli 53 Dec 17, 2022
Pneumonia Detection using machine learning - with PyTorch

Pneumonia Detection Pneumonia Detection using machine learning. Training was done in colab: DEMO: Result (Confusion Matrix): Data I uploaded my datase

Wilhelm Berghammer 12 Jul 07, 2022
Nodule Generation Algorithm Baseline and template code for node21 generation track

Nodule Generation Algorithm This codebase implements a simple baseline model, by following the main steps in the paper published by Litjens et al. for

node21challenge 10 Apr 21, 2022
MusicYOLO framework uses the object detection model, YOLOx, to locate notes in the spectrogram.

MusicYOLO MusicYOLO framework uses the object detection model, YOLOX, to locate notes in the spectrogram. Its performance on the ISMIR2014 dataset, MI

Xianke Wang 2 Aug 02, 2022
BARTScore: Evaluating Generated Text as Text Generation

This is the Repo for the paper: BARTScore: Evaluating Generated Text as Text Generation Updates 2021.06.28 Release online evaluation Demo 2021.06.25 R

NeuLab 196 Dec 17, 2022
Make your master artistic punk avatar through machine learning world famous paintings.

Master-art-punk Make your master artistic punk avatar through machine learning world famous paintings. 通过机器学习世界名画制作属于你的大师级艺术朋克头像 Nowadays, NFT is beco

Philipjhc 53 Dec 27, 2022
Goal of the project : Detecting Temporal Boundaries in Sign Language videos

MVA RecVis course final project : Goal of the project : Detecting Temporal Boundaries in Sign Language videos. Sign language automatic indexing is an

Loubna Ben Allal 6 Dec 21, 2022
Unoffical implementation about Image Super-Resolution via Iterative Refinement by Pytorch

Image Super-Resolution via Iterative Refinement Paper | Project Brief This is a unoffical implementation about Image Super-Resolution via Iterative Re

LiangWei Jiang 2.5k Jan 02, 2023
code for Image Manipulation Detection by Multi-View Multi-Scale Supervision

MVSS-Net Code and models for ICCV 2021 paper: Image Manipulation Detection by Multi-View Multi-Scale Supervision Update 22.02.17, Pretrained model for

dong_chengbo 131 Dec 30, 2022
UniFormer - official implementation of UniFormer

UniFormer This repo is the official implementation of "Uniformer: Unified Transf

SenseTime X-Lab 573 Jan 04, 2023
A framework for Quantification written in Python

QuaPy QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify) written in Python. QuaPy

41 Dec 14, 2022
Learning trajectory representations using self-supervision and programmatic supervision.

Trajectory Embedding for Behavior Analysis (TREBA) Implementation from the paper: Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Y

58 Jan 06, 2023
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

This is the official implementation of our paper: Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal. "CaTGrasp: Learning Category-Level Task-R

Bowen Wen 199 Jan 04, 2023
This is an official implementation for "PlaneRecNet".

PlaneRecNet This is an official implementation for PlaneRecNet: A multi-task convolutional neural network provides instance segmentation for piece-wis

yaxu 50 Nov 17, 2022
Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)

On Generating Transferable Targeted Perturbations (ICCV'21) Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli Paper:

Muzammal Naseer 46 Nov 17, 2022
'A C2C E-COMMERCE TRUST MODEL BASED ON REPUTATION' Python implementation

Project description A library providing functionalities to calculate reputation and degree of trust on C2C ecommerce platforms. The work is fully base

Davide Bigotti 2 Dec 14, 2022