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-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">

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">

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">

This dataset contains ILSVRC-2012 (ImageNet) validation images annotated with multi-class labels from "Evaluating Machine Accuracy on ImageNet", ICML, 2020. The multi-class labels were reviewed by a panel of experts extensively trained in the intricacies of fine-grained class distinctions in the ImageNet class hierarchy (see paper for more details). Compared to the original labels, these expert-reviewed multi-class labels enable a more semantically coherent evaluation of accuracy.

Version 3.0.0 of this dataset contains more corrected labels from "When does dough become a bagel? Analyzing the remaining mistakes on ImageNet as well as the ImageNet-Major (ImageNet-M) 68-example split under 'imagenet-m'.

Only 20,000 of the 50,000 ImageNet validation images have multi-label annotations. The set of multi-labels was first generated by a testbed of 67 trained ImageNet models, and then each individual model prediction was manually annotated by the experts as either correct (the label is correct for the image),wrong (the label is incorrect for the image), or unclear (no consensus was reached among the experts).

Additionally, during annotation, the expert panel identified a set of problematic images. An image was problematic if it met any of the below criteria:

• The original ImageNet label (top-1 label) was incorrect or unclear
• Image was a drawing, painting, sketch, cartoon, or computer-rendered
• Image was excessively edited
• Image had inappropriate content

The problematic images are included in this dataset but should be ignored when computing multi-label accuracy. Additionally, since the initial set of 20,000 annotations is class-balanced, but the set of problematic images is not, we recommend computing the per-class accuracies and then averaging them. We also recommend counting a prediction as correct if it is marked as correct or unclear (i.e., being lenient with the unclear labels).

One possible way of doing this is with the following NumPy code:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_multilabel', split='validation')

# We assume that predictions is a dictionary from file_name to a class index between 0 and 999

num_correct_per_class = {}
num_images_per_class = {}

for example in ds:
  # We ignore all problematic images
  if example[‘is_problematic’].numpy():
    continue

  # The label of the image in ImageNet
  cur_class = example['original_label'].numpy()

  # If we haven't processed this class yet, set the counters to 0
  if cur_class not in num_correct_per_class:
    num_correct_per_class[cur_class] = 0
    assert cur_class not in num_images_per_class
    num_images_per_class[cur_class] = 0

  num_images_per_class[cur_class] += 1

  # Get the predictions for this image
  cur_pred = predictions[example['file_name'].numpy()]

  # We count a prediction as correct if it is marked as correct or unclear
  # (i.e., we are lenient with the unclear labels)
  if cur_pred is in example['correct_multi_labels'].numpy() or cur_pred is in example['unclear_multi_labels'].numpy():
    num_correct_per_class[cur_class] += 1

# Check that we have collected accuracy data for each of the 1,000 classes
num_classes = 1000
assert len(num_correct_per_class) == num_classes
assert len(num_images_per_class) == num_classes

# Compute the per-class accuracies and then average them
final_avg = 0
for cid in range(num_classes):
 assert cid in num_correct_per_class
 assert cid in num_images_per_class
 final_avg += num_correct_per_class[cid] / num_images_per_class[cid]
final_avg /= num_classes

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_multilabel', 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_multilabel-3.0.0.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

flplv/imagenet-1k-Qwen2.5-VL-3B-Instruct-Labels dataset hosted on Hugging Face and contributed by the HF Datasets community

"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/).

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.

Dataset Description

"ImageNet Unique Label" (imagenet-ul) contains 5942 classes, which contains about 1 million images. The data undergoes a multi-step filtering process:

To ensure that all classes are not encountered during the pretraining of the vision model, To prevent the sharing of labels between two image classes, To exclude hyponyms from the label set, To ensure that each class contains at least 100 images.

It is a subset of ImageNet dataset (Russakovsky, O., Deng, J., Su… See the full description on the dataset page: https://huggingface.co/datasets/jaagli/imagenet-ul.

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">

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.

General Information

Title: ImageNet-AB Description: ImageNet-AB is an extended version of the ImageNet-1K training set, enriched with annotation byproducts (AB). In addition to the image and corresponding class labels, this dataset provides a rich history of interactions per input signal per front-end component during the annotation process. They include mouse traces, click locations, annotation times, as well as anonymised worker IDs. Links:

ICCV'23 Paper Main Repository ImageNet… See the full description on the dataset page: https://huggingface.co/datasets/coallaoh/ImageNet-AB.

Experimental comparison of various automatic image-labeling methods.

Comparison of annotation precision for single category labels using different algorithms.

This repository contains the mapping from integer id's to actual label names (in HuggingFace Transformers typically called id2label) for several datasets. Current datasets include:

ImageNet-1k ImageNet-22k (also called ImageNet-21k as there are 21,843 classes) COCO detection 2017 COCO panoptic 2017 ADE20k (actually, the MIT Scene Parsing benchmark, which is a subset of ADE20k) Cityscapes VQAv2 Kinetics-700 RVL-CDIP PASCAL VOC Kinetics-400 ...

You can read in a label file as follows (using… See the full description on the dataset page: https://huggingface.co/datasets/huggingface/label-files.

Comparison of image annotation methods in various experiments.

To enable research on automated alignment/interpretability evaluations, we release the experimental results of our paper "Scale Alone Does not Improve Mechanistic Interpretability in Vision Models" as a separate dataset.

Note that this is the first dataset containing interpretability measurements obtained through psychophysical experiments for multiple explanation methods and models. The dataset contains >120'000 anonymized human responses, each consisting of the final choice, a confidence score, and a reaction time. Out of these >120'000 responses, > 69'000 passed all our quality assertions - this is the main data (see responses_main.csv). The other responses failed (some) quality assertions and might be of lower quality - they should be used with care (see responses_lower_quality.csv). We consider the former the main dataset and provide the latter as data for development/debugging purposes. Furthermore, the dataset contains the used query images as well as the generated explanations for >760 units across nine models.

The dataset itself is a collection of labels and metainformation without the presence of fixed features that should be predictive of a unit's interpretability. Moreover, finding and constructing features that are predictive of the recorded labels will be one of the open challenges posed by this line of research.

Dataset Card for ImageNet_10k Dataset

This dataset is derived from ImageNet and contains 10,000 image-label pairs, designed for binary classification in object detection tasks.

  Dataset Details





  Dataset Description

This dataset consists of 10,000 image-label pairs sampled from ImageNet. 5,000 pairs have correct image-label matches (positive examples labeled "yes"), and 5,000 pairs have random labels assigned from the ImageNet 1000-class taxonomy (negative… See the full description on the dataset page: https://huggingface.co/datasets/Oztobuzz/ImageNet_10k.

This dataset consists of the ImageNet dataset resized to fixed size. The images here are the ones provided by Chrabaszcz et. al. using the box resize method.

For downsampled ImageNet for unsupervised learning see downsampled_imagenet.

WARNING: The integer labels used are defined by the authors and do not match those from the other ImageNet datasets provided by Tensorflow datasets. See the original label list, and the labels used by this dataset. Additionally, the original authors 1 index there labels which we convert to 0 indexed by subtracting one.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet_resized', 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_resized-8x8-0.1.0.png" alt="Visualization" width="500px">