Language-Driven Semantic Segmentation

Related tags

Deep Learninglang-seg
Overview

Language-driven Semantic Segmentation (LSeg)

The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation.

Authors:

Overview

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., ''grass'' or 'building'') together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (e.g., ''cat'' and ''furry''). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample. We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided.

Please check our Video Demo (4k) to further showcase the capabilities of LSeg.

Usage

Installation

Option 1:

pip install -r requirements.txt

Option 2:

conda install ipython
pip install torch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2
pip install git+https://github.com/zhanghang1989/PyTorch-Encoding/
pip install pytorch-lightning==1.3.5
pip install opencv-python
pip install imageio
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
pip install altair
pip install streamlit
pip install --upgrade protobuf
pip install timm
pip install tensorboardX
pip install matplotlib
pip install test-tube
pip install wandb

Running interactive app

Download the model for demo and put it under folder checkpoints as checkpoints/demo_e200.ckpt.

Then streamlit run lseg_app.py

Training

Backbone = ViT-L/16, Text Encoder from CLIP ViT-B/32

bash train.sh

Testing

Backbone = ViT-L/16, Text Encoder from CLIP ViT-B/32

bash test.sh

Try demo model

Download the model for demo and put it under folder checkpoints as checkpoints/demo_e200.ckpt.

Then follow lseg_demo.ipynb to play around with LSeg. Enjoy!

Model Zoo

name backbone text encoder url
0 Model for demo ViT-L/16 CLIP ViT-B/32 download

If you find this repo useful, please cite:

@article{li2022lan,
  title={Language-driven Semantic Segmentation},
  author={Li, Boyi and Weinberger, Kilian Q and Belongie, Serge and Koltun, Vladlen and Ranftl, Rene},
  journal={arXiv preprint},
  year={2022}
}

Acknowledgement

Thanks to the code base from DPT, Pytorch_lightning, CLIP, Pytorch Encoding, Streamlit, Wandb

Owner
Intelligent Systems Lab Org
Intelligent Systems Lab Org
Azua - build AI algorithms to aid efficient decision-making with minimum data requirements.

Project Azua 0. Overview Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can re

Microsoft 197 Jan 06, 2023
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.

Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi

18 Oct 20, 2022
Display, filter and search log messages in your terminal

Textualog Display, filter and search logging messages in the terminal. This project is powered by rich and textual. Some of the ideas and code in this

Rik Huygen 24 Dec 10, 2022
implicit displacement field

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
Use tensorflow to implement a Deep Neural Network for real time lane detection

LaneNet-Lane-Detection Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "To

MaybeShewill-CV 1.9k Jan 08, 2023
Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

Graph Notebook: easily query and visualize graphs The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Us

Amazon Web Services 501 Dec 28, 2022
GAN-generated image detection based on CNNs

GAN-image-detection This repository contains a GAN-generated image detector developed to distinguish real images from synthetic ones. The detector is

Image and Sound Processing Lab 17 Dec 15, 2022
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

MixText This repo contains codes for the following paper: Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden

GT-SALT 309 Dec 12, 2022
Vehicle direction identification consists of three module detection , tracking and direction recognization.

Vehicle-direction-identification Vehicle direction identification consists of three module detection , tracking and direction recognization. Algorithm

5 Nov 15, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 02, 2022
Robotics environments

Robotics environments Details and documentation on these robotics environments are available in OpenAI's blog post and the accompanying technical repo

Farama Foundation 121 Dec 28, 2022
[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)

Adversarial Attacks on Graph Classification via Bayesian Optimisation @ NeurIPS 2021 This repository contains the official implementation of GRABNEL,

Xingchen Wan 12 Dec 23, 2022
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

SEDE SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description

Rupert. 83 Nov 11, 2022
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

xmu-xiaoma66 7.7k Jan 05, 2023
An executor that performs image segmentation on fashion items

ClothingSegmenter U2NET fashion image/clothing segmenter based on https://github.com/levindabhi/cloth-segmentation Overview The ClothingSegmenter exec

Jina AI 5 Mar 30, 2022
PSANet: Point-wise Spatial Attention Network for Scene Parsing, ECCV2018.

PSANet: Point-wise Spatial Attention Network for Scene Parsing (in construction) by Hengshuang Zhao*, Yi Zhang*, Shu Liu, Jianping Shi, Chen Change Lo

Hengshuang Zhao 217 Oct 30, 2022
Platform-agnostic AI Framework 🔥

🇬🇧 TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework progra

TensorLayer Community 171 Jan 06, 2023
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

Mixup-Data-Dependency Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training". Running Alternating Line Exp

Muthu Chidambaram 0 Nov 11, 2021