Pytorch ImageNet1k Loader with Bounding Boxes.

Related tags

Deep Learning ImageNet1KBoundingBoxes
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.

iThermAI 4 Nov 27, 2022
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

Jacobi(Jiabo He) 147 Dec 5, 2022
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

Gabriel Nützi 390 Dec 31, 2022
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.

null 305 Dec 16, 2022
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

null 2 Apr 14, 2022
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

IM Lab., POSTECH 0 Sep 28, 2022
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

Enrico Fini 48 Sep 27, 2022
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

Simo Ryu 90 Dec 8, 2022
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,

Luca Moschella 520 Dec 30, 2022
Releases(files)
Owner
Amin Ghiasi
Amin Ghiasi
GitHub
labelpix is a graphical image labeling interface for drawing bounding boxes

Welcome to labelpix ?? labelpix is a graphical image labeling interface for drawing bounding boxes. ?? Homepage Install pip install -r requirements.tx

schissmantics 26 May 24, 2022
This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

SLATE This is the official source code for SLATE. We provide the code for the model, the training code and a dataset loader for the 3D Shapes dataset.

Gautam Singh 66 Dec 26, 2022
A Pytorch loader for MVTecAD dataset.

MVTecAD A Pytorch loader for MVTecAD dataset. It strictly follows the code style of common Pytorch datasets, such as torchvision.datasets.CIFAR10. The

Jiyuan 1 Dec 27, 2021
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation The paper: https://arxiv.org/abs/1704.03296 What makes

Jacob Gildenblat 322 Dec 17, 2022
IDA file loader for UF2, created for the DEFCON 29 hardware badge

UF2 Loader for IDA The DEFCON 29 badge uses the UF2 bootloader, which conveniently allows you to dump and flash the firmware over USB as a mass storag

Kevin Colley 6 Feb 8, 2022
SysWhispers Shellcode Loader

Shhhloader Shhhloader is a SysWhispers Shellcode Loader that is currently a Work in Progress. It takes raw shellcode as input and compiles a C++ stub

icyguider 630 Jan 3, 2023
This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes a

xinzelee 226 Jan 5, 2023
This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Rotate-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Section I. Description The codes are

xinzelee 90 Dec 13, 2022
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi

null 336 Dec 25, 2022