This dataset containing 500-999 classes of ImageNet Is part of the Imagenet dataset, all parts are: ImageNet-1k-0 - https://www.kaggle.com/datasets/sautkin/imagenet1k0 (0-499 classes); ImageNet-1k-1 - this; ImageNet-1k-2 - https://www.kaggle.com/datasets/sautkin/imagenet1k2 (0-499 classes); ImageNet-1k-3 - https://www.kaggle.com/datasets/sautkin/imagenet1k3 (500-999 classes); ImageNet-1k-valid - https://www.kaggle.com/datasets/sautkin/imagenet1kvalid (0-999 classes, test part)

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 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… See the full description on the dataset page: https://huggingface.co/datasets/ILSVRC/imagenet-1k.

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', 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/imagenet2012-5.1.0.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.

Dataset

This dataset was created by Giba

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Nikhil Shingadiya

Contents

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.

  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… See the full description on the dataset page: https://huggingface.co/datasets/benjamin-paine/imagenet-1k-256x256.

Dataset Card for "imagenet_1k_resized_256"

  Dataset summary

The same ImageNet dataset but all the smaller side resized to 256. A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen. The resized dataset can also be downloaded much faster and consume less space than the original one. See here for detailed readme.

  Dataset Structure

Below is the example of one row of data. Note that the labels in… See the full description on the dataset page: https://huggingface.co/datasets/evanarlian/imagenet_1k_resized_256.

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. Currently we have an average of over five hundred images per node. We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures. 版權聲明 https://www.image-net.org/download.php Terms of access: 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: Researcher shall use the Database only for non-commercial research and educational purposes. 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. 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. 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. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time. 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. The law of the State of New Jersey shall apply to all disputes under this agreement. 資料使用聲明 請參閱『申請』中之說明事項。

Randomly selected 10 images from each of the 1000 classes of images from the original Imagenet Dataset at ImageNet Object Localization Challenge. Total no. of samples thus becomes 10,000, which can be used for further analysis, if you prefer to use a smaller subset rather than the original. Download the original labels using api command: "kaggle competitions download imagenet-object-localization-challenge -f LOC_synset_mapping.txt"

Dataset

This dataset was created by Kerri

Contents

ImageNet-A is a set of images labelled with ImageNet labels that were obtained 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_a', 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_a-0.1.0.png" alt="Visualization" width="500px">

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).

A BitTorrent file to download data with the title 'ImageNet LSVRC 2012 Training Set (Object Detection)'

Dataset Summary

This is a copy of the full ImageNet dataset consisting of all of the original 21841 clases. It also contains labels in a separate field for the '12k' subset described at at (https://github.com/rwightman/imagenet-12k, https://huggingface.co/datasets/timm/imagenet-12k-wds) This dataset is from the original fall11 ImageNet release which has been replaced by the winter21 release which removes close to 3000 synsets containing people, a number of these are of an offensive… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-22k-wds.

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. It contains data from 2012 until 2017. The data is available for free to researchers for non-commercial use on the data provider's website.

For access to the full ImageNet dataset and other commonly used subsets, please login or request access on the website of the data providers. In doing so, you will need to agree to the ImageNet's terms of access. Therefore, no data preview can be provided here.

When reporting results of the challenges or using the datasets, please cite:

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. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.

File Descriptions

1) ILSVRC/ contains the image data and ground truth for the train and validation sets, and the image data for the test set.

• The image annotations are saved in XML files in PASCAL VOC format. Users can parse the annotations using the PASCAL Development Toolkit.
• Annotations are ordered by their synsets (for example, "Persian cat", "mountain bike", or "hot dog") as their wnid. These id's look like n00141669. Each image's name has direct correspondence with the annotation file name. For example, the bounding box for n02123394/n02123394_28.xml is n02123394_28.JPEG.
• You can download all the bounding boxes of a particular synset from http://www.image-net.org/api/download/imagenet.bbox.synset?wnid=[wnid]
• The training images are under the folders with the names of their synsets. The validation images are all in the same folder. The test images are also all in the same folder.
• ImageSet folder contains text files specifying lists of images for the main localization task.

2) LOC_sample_submission.csv is the correct format of the submission file. It contains two columns:

• ImageId: the id of the test image, for example ILSVRC2012_test_00000001
• PredictionString: the prediction string should be a space delimited of 5 integers. For example, 1000 240 170 260 240 means it's label 1000, with a bounding box of coordinates (x_min, y_min, x_max, y_max). We accept up to 5 predictions. For example, if you submit 862 42 24 170 186 862 292 28 430 198 862 168 24 292 190 862 299 238 443 374 862 160 195 294 357 862 3 214 135 356 which contains 6 bounding boxes, we will only take the first 5 into consideration.

3) LOC_train_solution.csv and LOC_val_solution.csv: These information are available in ILSVRC/ already, but we are providing them in csv format to be consistent with LOC_sample_submission.csv. Each file contains two columns:

• ImageId: the id of the train/val image, for example n02017213_7894 or ILSVRC2012_val_00048981
• PredictionString: the prediction string is a space delimited of 5 integers. For example, n01978287 240 170 260 240 means it's label n01978287, with a bounding box of coordinates (x_min, y_min, x_max, y_max). Repeated bounding boxes represent multiple boxes in the same image: n04447861 248 177 417 332 n04447861 171 156 251 175 n04447861 24 133 115 254

4) LOC_synset_mapping.txt: The mapping between the 1000 synset id and their descriptions. For example, Line 1 says n01440764 tench, Tinca tinca means this is class 1, has a synset id of n01440764, and it contains the fish tench.

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">

ImageNet-1k-512

A preprocessed dataset of ImageNet-1k for image generation. Each image is resized to 512x512. Follow the instructions here to download and use this dataset.

A BitTorrent file to download data with the title 'ImageNet LSVRC 2012 Validation Set (Object Detection)'

"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/).