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.

organized into multiple categories

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">

"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/).

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"

The Tiny-ImageNet dataset is a subset of the ImageNet dataset.

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

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

This dataset is used to evaluate the performance of a Convolutional Neural Network (CNN) on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2012).

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">

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

Dataset Card for "ImageNet-Hard"

Project Page - ArXiv - Paper - Github - Image Browser

  Dataset Summary

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… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/imagenet-hard.

Overview

This dataset contains features extracted from the Imagenet dataset using Local Binary Patterns Histograms. The LBP algorithm is configured with params p=8, r=1, gridX=8 and gridY=8. 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 ResNet
• Imagenet extracted features with Inception V3

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.

Dataset Card for "Imagenet-Hard-4K"

Project Page - Paper - Github ImageNet-Hard-4K is 4K version of the original ImageNet-Hard dataset, which 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… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/imagenet-hard-4K.

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

Example

https://www.kaggle.com/rafazz/starter-how-to-use-tinyimagenet-normalized

Context

The dataset is the 64x64 tiny counterpart for the ImageNet challenge (ILSVRC). This dataset is suitable for in-house experimentation, without hundreds of gigabytes of downloaded images.

Note

This dataset requires the https://github.com/z-a-f/zaf_funcs functions to be used.

Content

The dataset is a pickled dataset class and a dataloader. The images are normalized to 255.0 and to mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]. The images are converted to PyTorch tensors permuted into NCHW layout. The run-time transformation (in train mode) includes horizontal flipping with p=0.5.

Acknowledgements

The raw images could be downloaded from https://tiny-imagenet.herokuapp.com/, and all the credit goes to the CS231n peeps.

Overview

This dataset contains features extracted from the Imagenet dataset using a pre-trained VGG-19 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 LBP
• Imagenet extracted features with ResNet
• Imagenet extracted features with Inception V3

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

The Tiny ImageNet Dataset is a dataset of 100,000 tiny (64x64) images of objects. It is a popular dataset for image classification and object detection research. The dataset consists of 200 different classes, each of which has 500 images.