Dataset Summary

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. 💡… See the full description on the dataset page: https://huggingface.co/datasets/mlx-vision/imagenet-1k.

Dataset Summary

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. 💡… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-1k-wds.

Repack Information

This repository contains a complete repack of ILSVRC/imagenet-1k in Parquet format with the following data transformations:

Images were center-cropped to square to the minimum height/width dimension. Images were then rescaled to 256x256 using Lanczos resampling. This dataset is available at benjamin-paine/imagenet-1k-256x256 Images were then rescaled to 128x128 using Lanczos resampling.

  Dataset Card for ImageNet







  Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/benjamin-paine/imagenet-1k-128x128.

"ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use." (https://www.image-net.org/index.php)

I do not hold any copyright to this dataset. This data is just a re-distribution of the data Imagenet.org shared on Kaggle. Please note that some of the ImageNet1K images are under copyright.

This version of the data is directly sourced from Kaggle, excluding the bounding box annotations. Therefore, only images and class labels are included.

All images are resized to 256 x 256.

Integer labels are assigned after ordering the class names alphabetically.

Please note that anyone using this data abides by the original terms: ``` RESEARCHER_FULLNAME has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:

1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement.

The images are processed using [TPU VM](https://cloud.google.com/tpu/docs/users-guide-tpu-vm) via the support of Google's [TPU Research Cloud](https://sites.research.google/trc/about/).

Visualize on Visual Layer

  Imagenet-1K-VL-Enriched

An enriched version of the ImageNet-1K Dataset with image caption, bounding boxes, and label issues! With this additional information, the ImageNet-1K dataset can be extended to various tasks such as image retrieval or visual question answering. The label issues helps to curate a cleaner and leaner dataset.

  Description

The dataset consists of 6 columns:

image_id: The original filename of the image from… See the full description on the dataset page: https://huggingface.co/datasets/visual-layer/imagenet-1k-vl-enriched.

The dataset used in the paper is ILSVRC2012 (ImageNet 1K), a large-scale image classification dataset.

Repack Information

This repository contains a complete repack of ILSVRC/imagenet-1k in Parquet format, with no arbitrary code execution. Images were not resampled.

  Dataset Card for ImageNet





  Dataset Summary

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than… See the full description on the dataset page: https://huggingface.co/datasets/benjamin-paine/imagenet-1k.

ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which contains more pictures and classes, is used less frequently for pretraining, mainly due to its complexity, and underestimation of its added value compared to standard ImageNet-1K pretraining. This paper aims to close this gap, and make high-quality efficient pretraining on ImageNet-21K available for everyone. Via a dedicated preprocessing stage, utilizing WordNet hierarchies, and a novel training scheme called semantic softmax, we show that different models, including small mobile-oriented models, significantly benefit from ImageNet-21K pretraining on numerous datasets and tasks. We also show that we outperform previous ImageNet-21K pretraining schemes for prominent new models like ViT. Our proposed pretraining pipeline is efficient, accessible, and leads to SoTA reproducible results, from a publicly available dataset.

Imagenet 1k_tennis Table Ball

## Overview

Imagenet 1k_tennis Table Ball is a dataset for object detection tasks - it contains Ping Pong Ball annotations for 837 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ImageNet-1k's covariance matrix's eigenvalues (eigenvalues_ipca.npy), the ratio of total variance explained by each of ImageNet-1k's principal component (eigenvalues_ratio_ipca.npy), ImageNet-1k's principal components (pc_matrix_ipca.npy) computed using the normalized training dataset. For computational reasons, only 10% of the training dataset was used for PCA and only the top 20k principal components were computed. These items were used in [1]. The ImageNet-1k dataset was presented in [2]. [1] Alice Bizeul, Thomas M. Sutter, Alain Ryser, Julius Von Kügelgen, Bernhard Schölkopf, Julia E. Vogt. Components Beat Patches: Eigenvector Masking for Visual Representation Learning. Oct, 2024. [2] Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009.

The NINCO (No ImageNet Class Objects) dataset is introduced in the ICML 2023 paper In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K .

The NINCO main dataset consists of 64 OOD classes with a total of 5879 samples. These OOD classes were selected to have no categorical overlap with any classes of ImageNet-1K. Each sample was inspected individually by the authors to not contain ID objects.

Besides NINCO, included are (in the same .tar.gz file) truly OOD versions of 11 popular OOD datasets with in total 2715 OOD samples.

Further included are 17 OOD unit-tests, with 400 samples each.

Code for loading and evaluating on each of the three datasets is provided at https://github.com/j-cb/NINCO.

When using NINCO, please consider citing (besides the bibtex given below) the following data sources that were used to create NINCO:

• Hendrycks et al.: ”Scaling out-of-distribution detection for real-world settings”, ICML, 2022.
• Bossard et al.: ”Food-101 – mining discriminative components with random forests”, ECCV 2014.
• Zhou et al.: ”Places: A 10 million image database for scene recognition”, IEEE PAMI 2017.
• Huang et al.: ”Mos: Towards scaling out-of-distribution detection for large semantic space”, CVPR 2021.
• Li et al.: ”Caltech 101 (1.0)”, 2022.
• Ismail et al.: ”MYNursingHome: A fully-labelled image dataset for indoor object classification.”, Data in Brief (V. 32) 2020.
• The iNaturalist project: https://www.inaturalist.org/

When using NINCO_popular_datasets_subsamples, additionally to the above, please consider citing:

• Cimpoi et al.: ”Describing textures in the wild”, CVPR 2014.
• Hendrycks et al.: ”Natural adversarial examples”, CVPR 2021.
• Wang et al.: ”Vim: Out-of-distribution with virtual-logit matching”, CVPR 2022.
• Bendale et al.: ”Towards Open Set Deep Networks”, CVPR 2016.
• Vaze et al.: ”Open-set Recognition: a Good Closed-set Classifier is All You Need?”, ICLR 2022.
• Wang et al.: ”Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.” ICML, 2022.
• Galil et al.: “A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet”, ICLR 2023.
  

For citing our paper, we would appreciate using the following bibtex entry (this will be updated once the ICML 2023 proceedings are public):

@inproceedings{
bitterwolf2023ninco,
title={In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation},
author={Julian Bitterwolf and Maximilian Mueller and Matthias Hein},
booktitle={ICML},
year={2023},
url={https://proceedings.mlr.press/v202/bitterwolf23a.html}
}

Results on IMAGENET-100.

Dataset

This dataset was created by Konstantin Lopukhin

Released under CC0: Public Domain

Contents

SteveZeyuZhang/ImageNet-1K dataset hosted on Hugging Face and contributed by the HF Datasets community

ImageNet-R is a set of images labelled with ImageNet labels that were obtained by collecting art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions of ImageNet classes. ImageNet-R has renditions of 200 ImageNet classes resulting in 30,000 images. by collecting new data and keeping only those images that ResNet-50 models fail to correctly classify. For more details please refer to the paper.

The label space is the same as that of ImageNet2012. Each example is represented as a dictionary with the following keys:

• 'image': The image, a (H, W, 3)-tensor.
• 'label': An integer in the range [0, 1000).
• 'file_name': A unique sting identifying the example within the dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet_r', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/imagenet_r-0.2.0.png" alt="Visualization" width="500px">

NexaAI/ImageNet-1k dataset hosted on Hugging Face and contributed by the HF Datasets community

Accuracy in CIFAR-100 dataset and comparison with other methods.

Accuracy in Flowers-102 dataset and comparison with other methods.

Comparison between two methods, HybridBranchNetv2 and HybridBranchNet.

fengyang0317/imagenet-1k dataset hosted on Hugging Face and contributed by the HF Datasets community