Mae segmentation - Reproduction of semantic segmentation using masked autoencoder (mae)

Last update: Dec 17, 2022

Overview

ADE20k Semantic segmentation with MAE

Getting started

Install the mmsegmentation library and some required packages.

pip install mmcv-full==1.3.0 mmsegmentation==0.11.0
pip install scipy timm==0.3.2

Install apex for mixed-precision training

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Follow the guide in mmseg to prepare the ADE20k dataset.

Fine-tuning for Reproducing Results of MAE ViT-Base

Command:

tools/dist_train.sh configs/mae/upernet_mae_base_12_512_slide_160k_ade20k.py 8 --seed 0  --options model.pretrained=https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth

Expected results log(paper results: 48.1 mIoU):

+--------+-------+-------+-------+
| Scope  | mIoU  | mAcc  | aAcc  |
+--------+-------+-------+-------+
| global | 48.15 | 58.99 | 83.05 |
+--------+-------+-------+-------+

Evaluation

Command format:

tools/dist_test.sh  <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU

Acknowledgment

This code is built using the mmsegmentation library, Timm library, the Swin repository, XCiT, SETR, BEiT and the MAE repository.

Mae segmentation - Reproduction of semantic segmentation using masked autoencoder (mae)

Related tags

Overview

ADE20k Semantic segmentation with MAE

Getting started

Fine-tuning for Reproducing Results of MAE ViT-Base

Evaluation

Acknowledgment

Owner

Repository for the paper "From global to local MDI variable importances for random forests and when they are Shapley values"

An official PyTorch implementation of the TKDE paper "Self-Supervised Graph Representation Learning via Topology Transformations".

The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Training a deep learning model on the noisy CIFAR dataset

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

AirCode: A Robust Object Encoding Method

Wenzhou-Kean University AI-LAB

Clustering with variational Bayes and population Monte Carlo

Python implementation of "Elliptic Fourier Features of a Closed Contour"

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

PyTorch implementation of MLP-Mixer

OpenMMLab Model Deployment Toolset

A semismooth Newton method for elliptic PDE-constrained optimization

Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

This program automatically runs Python code copied in clipboard

This is the winning solution of the Endocv-2021 grand challange.

Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)