Applying PVT to Semantic Segmentation

Related tags

Deep Learning PVTv2-Seg
Overview

Applying PVT to Semantic Segmentation

Here, we take MMSegmentation v0.13.0 as an example, applying PVTv2 to SemanticFPN.

For details see Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions.

If you use this code for a paper please cite:

@misc{wang2021pyramid,
      title={Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions}, 
      author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and Tong Lu and Ping Luo and Ling Shao},
      year={2021},
      eprint={2102.12122},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Usage

Install MMSegmentation.

Data preparation

First, prepare ADE20K according to the guidelines in MMSegmentation.

Then, download the weights pretrained on ImageNet at here, and put them in a folder pretrained/

Results and models

Backbone Iters mIoU Config Download
PVTv2-B0 + Semantic FPN 40K 37.2 config
PVTv2-B1 + Semantic FPN 40K 42.5 config
PVTv2-B2 + Semantic FPN 40K 45.2 config
PVTv2-B3 + Semantic FPN 40K 47.3 config
PVTv2-B4 + Semantic FPN 40K 47.9 config
PVTv2-B5 + Semantic FPN 40K 48.7 config

Evaluation

To evaluate PVTv2-B2 + SemFPN on a single node with 8 gpus run:

dist_test.sh configs/sem_fpn/PVT/fpn_pvtv2_b2_ade20k_40k.py /path/to/checkpoint_file 8 --out results.pkl --eval mIoU

Training

To train PVTv2-B2 + SemFPN on a single node with 8 gpus run:

dist_train.sh configs/sem_fpn/PVT/fpn_pvtv2_b2_ade20k_40k.py 8

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

You might also like...
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF
Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Semantic-NeRF: Semantic Neural Radiance Fields Project Page | Video | Paper | Data In-Place Scene Labelling and Understanding with Implicit Scene Repr

Shuaifeng Zhi 243 Jan 7, 2023
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans

Abhay Gupta 161 Dec 8, 2022
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation Exploring Cross-Image Pixel Contrast for Semantic Segmentation, Wenguan Wang, Tianfei Z

Tianfei Zhou 510 Jan 2, 2023
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals.

Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals This repo contains the Pytorch implementation of our paper: Unsupervised Seman

Wouter Van Gansbeke 335 Dec 28, 2022
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

zhaohu xing 112 Dec 16, 2022
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

Microsoft 247 Dec 25, 2022
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

klein 125 Jan 3, 2023
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)

wsss-analysis The code of: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, arXiv pre-print 2019 paper.

Lyndon Chan 48 Dec 18, 2022
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Fudan Zhang Vision Group 897 Jan 5, 2023
Comments
  • a question of loading checkpoints

    a question of loading checkpoints

    Hello I try to run the dist_test.sh and use [pvt_v2_b2.pth as pretrained ckeckpoints] and [configs/sem_fpn/PVTv2/fpn_pvtv2_b2_ade20k_40k.py] but get an error: Traceback (most recent call last): File "./test.py", line 159, in Traceback (most recent call last): File "./test.py", line 159, in main() File "./test.py", line 127, in main main() File "./test.py", line 127, in main model.CLASSES = checkpoint['meta']['CLASSES'] KeyErrormodel.CLASSES = checkpoint['meta']['CLASSES']: 'meta' KeyError: 'meta'

    And the model and loaded state dict do not match exactly too.

    opened by uuz21181522 2
  • How many iterations do you train to get the ade20k result

    How many iterations do you train to get the ade20k result

    Hi, I am curious on how many iterations are you training on ade20k result. I looked for Segformer paper using 160K iters to reach the performance of mIOU 37.4(MiT_B0). But I found you are using 40k to reach a competitive result (mIOU 37.2). Is that a typo or it is reaching a competitive performance with much less iterations? Also, could you provide the pretrained model for pvt_b0 to b5?

    Thanks

    opened by ChengHan111 1
Owner
null
GitHub
Image morphing without reference points by applying warp maps and optimizing over them.

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

Alex K 380 Dec 19, 2022
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon 연애혁명 and tried to transfer human faces to webtoon domain.

이상윤 64 Oct 19, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

Neural Magic 1.5k Dec 30, 2022
Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques

About Fuzzification Fuzzification helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-

gts3.org (SSLab@Gatech) 55 Oct 25, 2022
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Learning Pixel-level Semantic Affinity with Image-level Supervision This code is deprecated. Please see https://github.com/jiwoon-ahn/irn instead. Int

Jiwoon Ahn 337 Dec 15, 2022
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP Abstract: We introduce a method that allows to automatically se

Daniil Pakhomov 134 Dec 19, 2022
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

yifan liu 147 Dec 3, 2022
Mae segmentation - Reproduction of semantic segmentation using masked autoencoder (mae)

ADE20k Semantic segmentation with MAE Getting started Install the mmsegmentation

null 97 Dec 17, 2022
A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

A curated list of awesome resources related to Semantic Search?? and Semantic Similarity tasks.

null 224 Jan 4, 2023
Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Siamese Deep Neural Networks for Semantic Text Similarity PyTorch A repository c

Shahrukh Khan 32 Dec 15, 2022