cogsci13/Imagenet-Class dataset hosted on Hugging Face and contributed by the HF Datasets community

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-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">

Dataset

This dataset was created by Yuri Njathi

Released under MIT

Contents

Dataset

This dataset was created by Patrycja Sekuła

Contents

Explore the ImageNet Micro dataset, a curated subset of ImageNet Mini featuring 999 classes with approximately 30 training images and 4 validation/testing images per class.

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 Summary

This is a copy of the full Winter21 release of ImageNet in webdataset tar format with JPEG images. This release consists of 19167 classes, 2674 fewer classes than the original 21841 class Fall11 release of the full ImageNet. The classes were removed due to these concerns: https://www.image-net.org/update-sep-17-2019.php

  Data Splits

The full ImageNet dataset has no defined splits. This release follows that and leaves everything in the train split.… See the full description on the dataset page: https://huggingface.co/datasets/timm/imagenet-w21-wds.

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">

The ImageNet-10 dataset is a subset of the ImageNet-1K dataset, containing images from 10 classes.

ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries "sketch of _", where _ is the standard class name. We only search within the "black and white" color scheme. We initially query 100 images for every class, and then manually clean the pulled images by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then we augment the data set by flipping and rotating the images.

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.

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

ImageNet-50 Subset

This dataset contains the first 50 classes from ImageNet-1K with up to 1,000 images per class (where available).

  Dataset Statistics

Total Classes: 50 Total Images: 50000 Train/Val Split: 90%/10% Max Images per Class: 1000

  Dataset Structure

imagenet-50-subset/ ├── train/ │ ├── n01440764/ # tench │ │ ├── n01440764_1234.JPEG │ │ └── ... │ ├── n01443537/ # goldfish │ └── ... ├── val/ │ ├── n01440764/ │ ├── n01443537/ │ └──… See the full description on the dataset page: https://huggingface.co/datasets/Elriggs/imagenet-50-subset.

Tiny-ImageNet-R, is a down-sampled subset of ImageNet-R(enditions) imagenet-r. It contains roughly 12,000 samples categorized in 64 classes (a subset of Tiny-ImageNet classes), spread across multiple visual domains such as art, cartoons, sculptures, origami, graffiti, and embroidery.

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:

• Full size
• 320 px
• 160 px

Note: The v2 config correspond to the new 70/30 train/valid split (released in Dec 6 2019).

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenette', 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/imagenette-full-size-v2-1.0.0.png" alt="Visualization" width="500px">

Dataset Description

A mini version of ImageNet-1k with 100 of 1000 classes present. Unlike some 'mini' variants this one includes the original images at their original sizes. Many such subsets downsample to 84x84 or other smaller resolutions.

  Data Splits





  Train

50000 samples from ImageNet-1k train split

  Validation

10000 samples from ImageNet-1k train split

  Test

5000 samples from ImageNet-1k validation split (all 50 samples per class)… See the full description on the dataset page: https://huggingface.co/datasets/timm/mini-imagenet.

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.

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">

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.