The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

Total number of non-empty WordNet synsets: 21841 Total number of images: 14197122 Number of images with bounding box annotations: 1,034,908 Number of synsets with SIFT features: 1000 Number of images with SIFT features: 1.2 million

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+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.

The test split contains 100K images but no labels because no labels have been publicly released. We provide support for the test split from 2012 with the minor patch released on October 10, 2019. In order to manually download this data, a user must perform the following operations:

1. Download the 2012 test split available here.
2. Download the October 10, 2019 patch. There is a Google Drive link to the patch provided on the same page.
3. Combine the two tar-balls, manually overwriting any images in the original archive with images from the patch. According to the instructions on image-net.org, this procedure overwrites just a few images.

The resulting tar-ball may then be processed by TFDS.

To assess the accuracy of a model on the ImageNet test split, one must run inference on all images in the split, export those results to a text file that must be uploaded to the ImageNet evaluation server. The maintainers of the ImageNet evaluation server permits a single user to submit up to 2 submissions per week in order to prevent overfitting.

To evaluate the accuracy on the test split, one must first create an account at image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:

771 778 794 387 650
363 691 764 923 427
737 369 430 531 124
755 930 755 59 168

The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See labels.txt.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_subset', 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/imagenet2012_subset-1pct-5.0.0.png" alt="Visualization" width="500px">

A BitTorrent file to download data with the title 'ImageNet Large Scale Visual Recognition Challenge (V2017)'

organized into multiple categories

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images.

This dataset contains ILSVRC-2012 (ImageNet) validation images augmented with a new set of "Re-Assessed" (ReaL) labels from the "Are we done with ImageNet" paper, see https://arxiv.org/abs/2006.07159. These labels are collected using the enhanced protocol, resulting in multi-label and more accurate annotations.

Important note: about 3500 examples contain no label, these should be excluded from the averaging when computing the accuracy. One possible way of doing this is with the following NumPy code:

is_correct = [pred in real_labels[i] for i, pred in enumerate(predictions) if real_labels[i]]
real_accuracy = np.mean(is_correct)

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_real', 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/imagenet2012_real-1.0.0.png" alt="Visualization" width="500px">

Dataset Card for tiny-imagenet

  Dataset Summary

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.

  Languages

The class labels in the dataset are in English.

  Dataset Structure





  Data Instances

{ 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190, 'label': 15 }… See the full description on the dataset page: https://huggingface.co/datasets/zh-plus/tiny-imagenet.

"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/).

ImageNet-Hard is a new benchmark that comprises 10,980 images collected from various existing ImageNet-scale benchmarks (ImageNet, ImageNet-V2, ImageNet-Sketch, ImageNet-C, ImageNet-R, ImageNet-ReaL, ImageNet-A, and ObjectNet). This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly. As a result, even the most advanced models, such as CLIP-ViT-L/14@336px, struggle to perform well on this dataset, achieving a mere 2.02% accuracy.

Dataset Summary

This is a filtered copy of the full ImageNet dataset consisting of the top 11821 (of 21841) classes by number of samples. It has been used to pretrain a number of in12k models in timm. The code and metadata for building this dataset from the original full ImageNet can be found at https://github.com/rwightman/imagenet-12k NOTE: This subset was filtered from the original fall11 ImageNet release which has been replaced by the winter21 release which removes close to 3000… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-12k-wds.

This is a subset of ImageNet called "ImageNet16" more suited for cases with limited computational budget and faster experimentation.

Each class has 400 train images and 100 test images.

• Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here.

If used in your work please cite as follows:

C. Kyrkou, "Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3380827.

The classes corresponding to imagenet1K:

• n02009912 American_egret

• n02113624 toy_poodle

• n02123597 Siamese_cat

• n02132136 brown_bear

• n02504458 African_elephant

• n02690373 airliner

• n02835271 bicycle-built-for-two

• n02951358 canoe

• n03041632 cleaver

• n03085013 computer_keyboard

• n03196217 digital_clock

• n03977966 police_van

• n04099969 rocking_chair

• n04111531 rotisserie

• n04285008 sports_car

• n04591713 wine_bottle

From original map.txt

knife = n03041632

keyboard = n03085013

elephant = n02504458

bicycle = n02835271

airplane = n02690373

clock = n03196217

oven = n04111531

chair = n04099969

bear = n02132136

boat = n02951358

cat = n02123597

bottle = n04591713

truck = n03977966

car = n04285008

bird = n02009912

dog = n02113624

Folder Structure

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Some preliminary results:

Model Name Accuracy (Top-1)

VGG16 85.3

ResNet50 88.2

MobileNetV2 91.0

EfficientNet B0 85.6

Massive Credit to original ImageNet authors[1] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015

Overview

This dataset contains features extracted from the Imagenet dataset using a pre-trained ResNet neural network. The network was configured with an input layer of (200, 200, 3). Feature extraction was performed using the Python package Py Image Feature Extractor.

Source

• Imagenet dataset from Kaggle Competition

Related datasets

• Imagenet extracted features with VGG-19
• Imagenet extracted features with Inception V3
• Imagenet extracted features with LBP

iny Imagenet has 200 Classes, each class has 500 traininig images, 50 Validation Images and 50 test images. Label Classes and Bounding Boxes are provided. More details can be found at https://tiny-imagenet.herokuapp.com/",

This challenge is part of Stanford Class CS 231N

The ImageNet-A dataset consists of real-world, unmodified, and naturally occurring examples that are misclassified by ResNet models.

Introduction The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: * Person: person * Animal: bird, cat, cow, dog, horse, sheep * Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train * Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There are three main object recognition competitions: classification, detection, and segmentation, a competition on action classification, and a competition on large scale recognition run by ImageNet. In addition there is a "taster" competition on person layout. ##Classification/Detection Competitions Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image. Detection: Predicting the bounding b

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset is a large-scale image classification dataset containing over 14 million images from 21,841 categories.

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.

This ImageNet version contains only 50 training images per class while the original testing set remains unchanged. It is one of the datasets comprising the data-efficient image classification (DEIC) benchmark. It was proposed to challenge the generalization capabilities of modern image classifiers.

ImageNet

## Overview

ImageNet is a dataset for object detection tasks - it contains 1 annotations for 1,002 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).

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.