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

Related tags

Deep LearningMobileViT
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
GitHub Repository
Keywords : Streamlit, BertTokenizer, BertForMaskedLM, Pytorch

Next Word Prediction Keywords : Streamlit, BertTokenizer, BertForMaskedLM, Pytorch 🎬 Project Demo ✔ Application is hosted on Streamlit. You can see t

Vivek7 3 Aug 26, 2022
Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

53 Dec 02, 2022
HeartRate detector with ArduinoandPython - Use Arduino and Python create a heartrate detector.

Syllabus of Contents Syllabus of Contents Introduction Of Project Features Develop With Python code introduction Installation License Developer Contac

1 Jan 05, 2022
Python library containing BART query generation and BERT-based Siamese models for neural retrieval.

Neural Retrieval Embedding-based Zero-shot Retrieval through Query Generation leverages query synthesis over large corpuses of unlabeled text (such as

Amazon Web Services - Labs 35 Apr 14, 2022
iNAS: Integral NAS for Device-Aware Salient Object Detection

iNAS: Integral NAS for Device-Aware Salient Object Detection Introduction Integral search design (jointly consider backbone/head structures, design/de

顾宇超 77 Dec 02, 2022
BRepNet: A topological message passing system for solid models

BRepNet: A topological message passing system for solid models This repository contains the an implementation of BRepNet: A topological message passin

Autodesk AI Lab 42 Dec 30, 2022
Python implementation of Bayesian optimization over permutation spaces.

Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o

Aryan Deshwal 9 Dec 23, 2022
Discover hidden deepweb pages

DeepWeb Scapper Att: Demo version An simple script to scrappe deepweb to find pages. Will return if any of those exists and will save on a file. You s

Héber Júlio 77 Oct 02, 2022
A curated list of awesome Model-Based RL resources

Awesome Model-Based Reinforcement Learning This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository w

OpenDILab 427 Jan 03, 2023
Iran Open Source Hackathon

Iran Open Source Hackathon is an open-source hackathon (duh) with the aim of encouraging participation in open-source contribution amongst Iranian dev

OSS Hackathon 121 Dec 25, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
True per-item rarity for Loot

True-Rarity True per-item rarity for Loot (For Adventurers) and More Loot A.K.A mLoot each out/true_rarity_{item_type}.json file contains probabilitie

Dan R. 3 Jul 26, 2022
KGDet: Keypoint-Guided Fashion Detection (AAAI 2021)

KGDet: Keypoint-Guided Fashion Detection (AAAI 2021) This is an official implementation of the AAAI-2021 paper "KGDet: Keypoint-Guided Fashion Detecti

Qian Shenhan 35 Dec 29, 2022
Hypernetwork-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le

Sungmin Hong 6 Jul 18, 2022
Wordplay, an artificial Intelligence based crossword puzzle solver.

Wordplay, AI based crossword puzzle solver A crossword is a word puzzle that usually takes the form of a square or a rectangular grid of white- and bl

Vaibhaw 4 Nov 16, 2022
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

79 Jan 06, 2023
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
Multi-Agent Reinforcement Learning (MARL) method to learn scalable control polices for multi-agent target tracking.

scalableMARL Scalable Reinforcement Learning Policies for Multi-Agent Control CD. Hsu, H. Jeong, GJ. Pappas, P. Chaudhari. "Scalable Reinforcement Lea

Christopher Hsu 17 Nov 17, 2022
Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021)

Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021) Introduction This is the official repository for the PyTorch implementation

165 Dec 07, 2022