Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

Overview

MobileViT

RegNet

Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER.


Table of Contents


Model Architecture

Trulli

MobileViT Architecture

Usage

Training

python main.py
optional arguments:
  -h, --help            show this help message and exit
  --gpu_device GPU_DEVICE
                        Select specific GPU to run the model
  --batch-size N        Input batch size for training (default: 64)
  --epochs N            Number of epochs to train (default: 20)
  --num-class N         Number of classes to classify (default: 10)
  --lr LR               Learning rate (default: 0.01)
  --weight-decay WD     Weight decay (default: 1e-5)
  --model-path PATH     Path to save the model

Citation

@InProceedings{Sachin2021,
  title = {MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TRANSFORMER},
  author = {Sachin Mehta and Mohammad Rastegari},
  booktitle = {},
  year = {2021}
}

If this implement have any problem please let me know, thank you.

Comments
  • Training settings

    Training settings

    I really appreciate your efforts in implementing this model in pytorch. Here, I have one concern about the training settings. If what I understand is correct, you just trained the model for less than 5 epoches.

    In addition, the hyper-parameters you adopted is different from that in the original article. For instance, in the original manuscript, authors train mobilevit using AdamW optimizer, label smoothing cross-entry and multi-scale sampler. The training phase has a warmup stage.

    I also found that the classificaion accuracy provided here is much lower than that in the original version.

    I conjecture that the gab between accuracies are caused by different training settings.

    opened by hkzhang91 6
  • load pretrain weight failed

    load pretrain weight failed

    import torch
    import models
    
    model = models.MobileViT_S()
    PATH = "./MobileVit-S.pth.tar"
    weights = torch.load(PATH, map_location=lambda storage, loc: storage)
    model.load_state_dict(weights['state_dict'])
    model.eval()
    torch.save(model, './model.pt')
    
    • I try to load the pre-train weight to test one demo; but the network structure does not seem to match the weights, is there any solution?

    image

    opened by hererookie 2
  • model training hyperparameter

    model training hyperparameter

    A problem has been bothering me. the learning rate, optimizer, batch_size, L2 regularization, label smoothing and epochs are inconsistent with the paper. How should I modify the code?

    opened by Agino-ltp 1
  • Have you test MobileVit on cifar-10?

    Have you test MobileVit on cifar-10?

    Thanks for your wonderful work!

    I prepare to try MobileVit on small dataset, such as MNIST, and I need adjust the network structure. Before this work, I want to know if MobileVit has a better performance than other networks on small dataset.

    I notice "get_cifar10_dataset" in utils.py. Have you tested MobileVit on cifar-10? If you have, could you please show me the accuracy and inference time result?

    opened by Jerryme-xxm 1
  • Issues when loading MobileViT_S()

    Issues when loading MobileViT_S()

    I wanted to load the MobileViT_S() model and use the pre-trained weights, but I have got some errors in my code. To make it easier and help others, I will share my solution (in case there will be someone who is beginner like me):

    def load_mobilevit_weights(model_path):
      # Create an instance of the MobileViT model
      net = MobileViT_S()
      
      # Load the PyTorch state_dict
      state_dict = torch.load(model_path, map_location=torch.device('cpu'))['state_dict']
      
      # Since there is a problem in the names of layers, we will change the keys to meet the MobileViT model architecture
      for key in list(state_dict.keys()):
        state_dict[key.replace('module.', '')] = state_dict.pop(key)
      
      # Once the keys are fixed, we can modify the parameters of MobileViT
      net.load_state_dict(state_dict)
      
      return net
    
    net = load_mobilevit_weights("MobileViT_S_model_best.pth.tar")
    
    opened by Sehaba95 4
Releases(weight)
Owner
Hong-Jia Chen
Master student at National Chung Cheng University, Taiwan. Interested in Deep Learning and Computer Vision.
Hong-Jia Chen
This is the official PyTorch implementation of our paper: "Artistic Style Transfer with Internal-external Learning and Contrastive Learning".

Artistic Style Transfer with Internal-external Learning and Contrastive Learning This is the official PyTorch implementation of our paper: "Artistic S

51 Dec 20, 2022
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
DM-ACME compatible implementation of the Arm26 environment from Mujoco

ACME-compatible implementation of Arm26 from Mujoco This repository contains a customized implementation of Mujoco's Arm26 model, that can be used wit

1 Dec 24, 2021
Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation This repository contains the official PyTorch implementation of the following

Wonjong Jang 270 Dec 30, 2022
Real-Time-Student-Attendence-System - Real Time Student Attendence System

Real-Time-Student-Attendence-System The Student Attendance Management System Pro

Rounak Das 1 Feb 15, 2022
Code for AutoNL on ImageNet (CVPR2020)

Neural Architecture Search for Lightweight Non-Local Networks This repository contains the code for CVPR 2020 paper Neural Architecture Search for Lig

Yingwei Li 104 Aug 31, 2022
Demo for Real-time RGBD-based Extended Body Pose Estimation paper

Real-time RGBD-based Extended Body Pose Estimation This repository is a real-time demo for our paper that was published at WACV 2021 conference The ou

Renat Bashirov 118 Dec 26, 2022
Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation".

FPS-Net Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation", accepted by ISPRS journal of Photogrammetry

15 Nov 30, 2022
[BMVC2021] The official implementation of "DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations"

DomainMix [BMVC2021] The official implementation of "DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations" [paper] [de

Wenhao Wang 17 Dec 20, 2022
Person Re-identification

Person Re-identification Final project of Computer Vision Table of content Person Re-identification Table of content Students: Proposed method Dataset

Nguyễn Hoàng Quân 4 Jun 17, 2021
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T

Danfeng Hong 154 Dec 13, 2022
ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners.

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥

AI4Finance 2.5k Jan 08, 2023
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

This dataset is a large-scale dataset for moving object detection and tracking in satellite videos, which consists of 40 satellite videos captured by Jilin-1 satellite platforms.

Qingyong 87 Dec 22, 2022
Official Python implementation of the 'Sparse deconvolution'-v0.3.0

Sparse deconvolution Python v0.3.0 Official Python implementation of the 'Sparse deconvolution', and the CPU (NumPy) and GPU (CuPy) calculation backen

Weisong Zhao 23 Dec 28, 2022
Cave Generation using metaballs in Blender. Originally created by sdfgeoff, Edited by Myself (Archie Jaskowicz).

Blender-Cave-Generation Cave Generation using metaballs in Blender. Originally created by sdfgeoff, Edited by Myself (Archie Jaskowicz). Installation

2 Dec 28, 2022
Implementation of QuickDraw - an online game developed by Google, combined with AirGesture - a simple gesture recognition application

QuickDraw - AirGesture Introduction Here is my python source code for QuickDraw - an online game developed by google, combined with AirGesture - a sim

Viet Nguyen 89 Dec 18, 2022
EXplainable Artificial Intelligence (XAI)

EXplainable Artificial Intelligence (XAI) This repository includes the codes for different projects on eXplainable Artificial Intelligence (XAI) by th

4 Nov 28, 2022
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

52 Nov 21, 2022
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

Lars Mescheder 885 Jan 01, 2023