Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Related tags

Deep Learning Bayesian-Amodal
Overview

Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model

Quick Start: Setup environment / Download models and dataset

conda env create -f environment.yml
conda activate amodal
bash download.sh
  • In the case that download.sh cannot be executed properly, please identify the missing directory and rerun the wget command for the corresponding zip file. If the issue persists, please refer to the download.sh description below.

Run experiments

  • Table 1: change the file Code/configs.py to set TABLE_NUM = 1 and MODEL_TYPE = 'ML' or MODEL_TYPE = 'E2E' and run the command below.

  • Table 2: change the file Code/configs.py to set TABLE_NUM = 2 and MODEL_TYPE = 'ML' or MODEL_TYPE = 'E2E' and run the command below.

  • Table 3: change the file Code/configs.py to set TABLE_NUM = 3 and MODEL_TYPE = 'ML' or MODEL_TYPE = 'E2E' and run the command below.

cd Code
python3 run_experiment.py

Optional: download.sh Description

Download models

  • Download pretrained model weights from here, unzip Models.zip and place the folder as /Models/.

  • Download RPN results used for evaluatiooon from here, unzip RPN_results.zip and place the folder as /RPN_results/.

Download dataset

  • Download Occluded Vehicle Dataset from here, unzip Occluded_Vehicles.zip and place the folder as /Dataset/Occluded_Vehicles/.

  • Download KINS Dataset from here, unzip kitti.zip and place the folder as /Dataset/kitti/.

  • Download COCOA Dataset from here, unzip COCO.zip and place the folder as /Dataset/COCO/. Additionally, download COCO data train2014 and val2014 and place the folders as /Dataset/COCO/train2014/ and /Dataset/COCO/val2014/.

You might also like...
aka
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Bayesian Methods for Hackers Using Python and PyMC The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chap

Cameron Davidson-Pilon 25.1k Jan 2, 2023
pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 4, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Intel Labs 210 Jan 4, 2023
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
Outlier Exposure with Confidence Control for Out-of-Distribution Detection
Outlier Exposure with Confidence Control for Out-of-Distribution Detection

OOD-detection-using-OECC This repository contains the essential code for the paper Outlier Exposure with Confidence Control for Out-of-Distribution De

Nazim Shaikh 64 Nov 2, 2022
Principled Detection of Out-of-Distribution Examples in Neural Networks
Principled Detection of Out-of-Distribution Examples in Neural Networks

ODIN: Out-of-Distribution Detector for Neural Networks This is a PyTorch implementation for detecting out-of-distribution examples in neural networks.

null 189 Nov 29, 2022
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.

SCOOD-UDG (ICCV 2021) This repository is the official implementation of the paper: Semantically Coherent Out-of-Distribution Detection Jingkang Yang,

Jake YANG 62 Nov 21, 2022
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

Wenxuan Zhou 27 Oct 28, 2022
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo

Udit Arora 19 Oct 28, 2022
Owner
Yihong Sun
Third-year Computer Science, Neuroscience, and Applied Math and Statistics triple major at Johns Hopkins University
Yihong Sun
GitHub
Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization

Fishr: Invariant Gradient Variances for Out-of-distribution Generalization Official PyTorch implementation of the Fishr regularization for out-of-dist

null 62 Dec 22, 2022
Official repository for CVPR21 paper "Deep Stable Learning for Out-Of-Distribution Generalization".

StableNet StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper "Deep Stable L

null 120 Dec 28, 2022
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

null 47 Dec 28, 2022
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation

Paper Khoi Nguyen, Sinisa Todorovic "A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation", accepted to ICCV 2021 Our code is mai

Khoi Nguyen 5 Aug 14, 2022
Hierarchical-Bayesian-Defense - Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference (Openreview)

Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical V

LBK 20 Dec 2, 2022
The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.

Codebase for training transformers on systematic generalization datasets. The official repository for our EMNLP 2021 paper The Devil is in the Detail:

Csordás Róbert 57 Nov 21, 2022
PyTorch evaluation code for Delving Deep into the Generalization of Vision Transformers under Distribution Shifts.

Out-of-distribution Generalization Investigation on Vision Transformers This repository contains PyTorch evaluation code for Delving Deep into the Gen

Chongzhi Zhang 72 Dec 13, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

null 100 Dec 28, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

null 100 Dec 28, 2022
Spearmint Bayesian optimization codebase

Spearmint Spearmint is a software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus the code n

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 1.5k Dec 29, 2022