Pytorch ImageNet1k Loader with Bounding Boxes.

Overview

ImageNet 1K Bounding Boxes

For some experiments, you might wanna pass only the background of imagenet images vs passing only the foreground. Here, I've included the code to extract the meta-data for the bounding box, cleaning up the the downloaded stuff, and then changing ImageNet Loader to support only the images that have box annotations.

How to use:

from costum_imagenet import BackgroundForegroundImageNet
tr = trans.Compose([trans.Resize(224), trans.CenterCrop(224), trans.ToTensor(), ])
dataset = BackgroundForegroundImageNet(root='./data/imagenet/train', download=True, transform=tr)
x, b, f, y = dataset[0]
torchvision.utils.save_image(torch.stack([x, b, f]), 'test1.png')

Example:

Test1 Test2

If you set the value download=True, the bounding boxes and the indices of imagenet train split that have the bounding boxes will be downloaded. But if for some reason you want to create your own bounding boxes from the scratch, here's the steps for doing it:

Restarting from the scratch

Downloading: First download the data from here:

wget "https://image-net.org/data/bboxes_annotations.tar.gz"

Extract the File:

tar -xvf bboxes_annotations.tar.gz 

Extract every subfolder:

cd bboxes_annotations
ls | grep .tar.gz | while read f ; do tar -xvf "${f}" ; done

Convert dataset to JS:

python read_xml.py 

Clean the extra 50GB extracted files:

rm *.tar.gz
ls | grep "n.*" | while read f ; do rm -rf "${f}"  ; done 

Get Indices that have bounding boxes:

python get_indices.py 

Then simply pass the path to the files boxes.pt and indices.pt to your BackgroundForegroundImageNet constructor

dataset = BackgroundForegroundImageNet(root='.', download=False, boxes='boxes.pt', indices='indices.pt')
You might also like...
This code finds bounding box of a single human mouth.
This code finds bounding box of a single human mouth.

This code finds bounding box of a single human mouth. In comparison to other face segmentation methods, it is relatively insusceptible to open mouth conditions, e.g., yawning, surgical robots, etc. The mouth coordinates are found in a more certified way using two independent algorithms. Therefore, the algorithm can be used in more sensitive applications.

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression YOLOv5 with alpha-IoU losses implemented in PyTorch. Example r

Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D.
Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D.

ApproxMVBB Status Build UnitTests Homepage Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and testing data for various deep learning projects such as 6D object pose estimation projects singleshotpose, as well as object detection and instance segmentation projects.

Improving Object Detection by Estimating Bounding Box Quality Accurately
Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstrac

LQM - Improving Object Detection by Estimating Bounding Box Quality Accurately
LQM - Improving Object Detection by Estimating Bounding Box Quality Accurately

Improving Object Detection by Estimating Bounding Box Quality Accurately Abstract Object detection aims to locate and classify object instances in ima

An essential implementation of BYOL in PyTorch + PyTorch Lightning
An essential implementation of BYOL in PyTorch + PyTorch Lightning

Essential BYOL A simple and complete implementation of Bootstrap your own latent: A new approach to self-supervised Learning in PyTorch + PyTorch Ligh

RealFormer-Pytorch Implementation of RealFormer using pytorch
RealFormer-Pytorch Implementation of RealFormer using pytorch

RealFormer-Pytorch Implementation of RealFormer using pytorch. Includes comparison with classical Transformer on image classification task (ViT) wrt C

Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.

NN Template Generic template to bootstrap your PyTorch project. Click on Use this Template and avoid writing boilerplate code for: PyTorch Lightning,

Releases(files)
Owner
Amin Ghiasi
Amin Ghiasi
[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

InvCompress Official Pytorch Implementation for "Enhanced Invertible Encoding for Learned Image Compression", ACMMM 2021 (Oral) Figure: Our framework

96 Nov 30, 2022
Devkit for 3D -- Some utils for 3D object detection based on Numpy and Pytorch

D3D Devkit for 3D: Some utils for 3D object detection and tracking based on Numpy and Pytorch Please consider siting my work if you find this library

Jacob Zhong 27 Jul 07, 2022
Repo for Photon-Starved Scene Inference using Single Photon Cameras, ICCV 2021

Photon-Starved Scene Inference using Single Photon Cameras ICCV 2021 Arxiv Project Video Bhavya Goyal, Mohit Gupta University of Wisconsin-Madison Abs

Bhavya Goyal 5 Nov 15, 2022
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
Repo for FUZE project. I will also publish some Linux kernel LPE exploits for various real world kernel vulnerabilities here. the samples are uploaded for education purposes for red and blue teams.

Linux_kernel_exploits Some Linux kernel exploits for various real world kernel vulnerabilities here. More exploits are yet to come. This repo contains

Wei Wu 472 Dec 21, 2022
Equivariant GNN for the prediction of atomic multipoles up to quadrupoles.

Equivariant Graph Neural Network for Atomic Multipoles Description Repository for the Model used in the publication 'Learning Atomic Multipoles: Predi

16 Nov 22, 2022
DP-CL(Continual Learning with Differential Privacy)

DP-CL(Continual Learning with Differential Privacy) This is the official implementation of the Continual Learning with Differential Privacy. If you us

Phung Lai 3 Nov 04, 2022
Distilled coarse part of LoFTR adapted for compatibility with TensorRT and embedded divices

Coarse LoFTR TRT Google Colab demo notebook This project provides a deep learning model for the Local Feature Matching for two images that can be used

Kirill 46 Dec 24, 2022
A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.

ViTGAN: Training GANs with Vision Transformers A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers. Refer

Hong-Jia Chen 127 Dec 23, 2022
Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
A port of muP to JAX/Haiku

MUP for Haiku This is a (very preliminary) port of Yang and Hu et al.'s μP repo to Haiku and JAX. It's not feature complete, and I'm very open to sugg

18 Dec 30, 2022
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration

This repo is for the paper: Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration The DAC environment is based on the Dynam

Carola Doerr 1 Aug 19, 2022
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of Learning to Reweight Examples for Robust Deep Learning. Th

Daniel Stanley Tan 325 Dec 28, 2022
Human Pose Detection on EdgeTPU

Coral PoseNet Pose estimation refers to computer vision techniques that detect human figures in images and video, so that one could determine, for exa

google-coral 476 Dec 31, 2022
TeST: Temporal-Stable Thresholding for Semi-supervised Learning

TeST: Temporal-Stable Thresholding for Semi-supervised Learning TeST Illustration Semi-supervised learning (SSL) offers an effective method for large-

Xiong Weiyu 1 Jul 14, 2022
Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.

Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur

Nicklas Hansen 101 Nov 01, 2022
Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows

Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows This is the official implementation of the ICCV 2021 Paper "Probabilistic Mono

62 Nov 23, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

ProcrustEs-KGE Paddle implementation for Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis 🙈 A more detailed re

Lincedo Lab 4 Jun 09, 2021