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

Last update: Nov 15, 2022

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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/.

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

Related tags

Overview

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

Quick Start: Setup environment / Download models and dataset

Run experiments

Optional: `download.sh` Description

Download models

Download dataset

Owner

Yihong Sun

3D-aware GANs based on NeRF (arXiv).

GrabGpu_py: a scripts for grab gpu when gpu is free

Reproduced Code for Image Forgery Detection papers.

Training data extraction on GPT-2

Voice of Pajlada with model and weights.

[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers

A working implementation of the Categorical DQN (Distributional RL).

This repository provides some of the code implemented and the data used for the work proposed in "A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems".

This is a clean and robust Pytorch implementation of DQN and Double DQN.

Basit bir burç modülü.

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Acoustic mosquito detection code with Bayesian Neural Networks

《Geo Word Clouds》paper implementation

Film review classification

Plenoxels: Radiance Fields without Neural Networks

Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

ADOP: Approximate Differentiable One-Pixel Point Rendering

A style-based Quantum Generative Adversarial Network

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

Related tags

Overview

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

Quick Start: Setup environment / Download models and dataset

Run experiments

Optional: download.sh Description

Download models

Download dataset

Owner

Yihong Sun

3D-aware GANs based on NeRF (arXiv).

GrabGpu_py: a scripts for grab gpu when gpu is free

Reproduced Code for Image Forgery Detection papers.

Training data extraction on GPT-2

Voice of Pajlada with model and weights.

[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers

A working implementation of the Categorical DQN (Distributional RL).

This repository provides some of the code implemented and the data used for the work proposed in "A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems".

This is a clean and robust Pytorch implementation of DQN and Double DQN.

Basit bir burç modülü.

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Acoustic mosquito detection code with Bayesian Neural Networks

《Geo Word Clouds》paper implementation

Film review classification

Plenoxels: Radiance Fields without Neural Networks

Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

ADOP: Approximate Differentiable One-Pixel Point Rendering

A style-based Quantum Generative Adversarial Network

Optional: `download.sh` Description