This is imagenet1k in webdataset format. Images are stored as jpg files. Every image has been resized to a maximum side length of 256. That means that if an image in the original dataset was 1000 by 500, the new size will be 256 by 128. Images with a maximum side length of under 256 were not resized. The total size of all dataset files is 57.8 GB, there are 1,281,167 rows in the training split and 50,000 rows in the validation split.

Maryamm/Imagenet1k dataset hosted on Hugging Face and contributed by the HF Datasets community

torch-uncertainty/Imagenet1k dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Kuihao Chang

Contents

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 quality-controlled and human-annotated. 💡… See the full description on the dataset page: https://huggingface.co/datasets/mlx-vision/imagenet-1k.

Dataset

This dataset was created by Joni Juvonen

Contents

Dataset

This dataset was created by motono0223

Released under CC0: Public Domain

Contents

wusize/imagenet1k 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/).

joshelb/imagenet1k dataset hosted on Hugging Face and contributed by the HF Datasets community

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

EfficientNetV2 TFHub Weight Files

License: Apache License 2.0

Updates: - Version 5 - Added XL weights. Updated to version 2 weights from tfhub.

Contents

✅ Feature Vector Generation Models ✅ Classification Models ✅ ImageNet 1K Pre-trained Weights ✅ ImageNet 21K Pre-trained Weights ✅ ImageNet 21K Pre-trained & 1k Fine-tuned Weights

Fixed weight files in v4. Please make sure to use v4 or above.

📌 The models are in TensorFlow 2 SavedModel format. Using it requires TensorFlow 2 (or 1.15) and TensorFlow Hub 0.5.0 or newer. Usage Notebook: https://www.kaggle.com/sreevishnudamodaran/siim-effnetv2-keras-study-train-tpu-cv0-805

📌 The model keys below with no suffixes are pretrained on ImageNet1K. The ones with the '21k' as the suffix are pretrained on ImageNet21K and the ones with '21k-ft1k' as the suffix are pretrained on ImageNet21K and then finetuned on ImageNet1K.

ImageNet1K pretrained and finetuned models: | ImageNet1K | Top1 Acc. | Params | FLOPs | Inference Latency | links | | ---------- | ------ | ------ | ------ | ------ | ------ | | EffNetV2-S | 83.9% | 21.5M | 8.4B | V100/A100 | ckpt, tensorboard | EffNetV2-M | 85.2% | 54.1M | 24.7B | V100/A100 | ckpt, tensorboard | EffNetV2-L | 85.7% | 119.5M | 56.3B | V100/A100 | ckpt, tensorboard

Models Pretrained on ImageNet21K pretrained and finetuned with ImageNet1K: | ImageNet21K | Pretrained models | Finetuned ImageNet1K | | ---------- | ------ | ------ | | EffNetV2-S | pretrain ckpt | top1=84.9%, ckpt, tensorboard | | EffNetV2-M | pretrain ckpt | top1=86.2%, ckpt, tensorboard | | EffNetV2-L | pretrain ckpt | top1=86.9%, ckpt, tensorboard |

Acknowledgements

https://tfhub.dev/ https://github.com/google/automl/blob/master/efficientnetv2

aoi-ot/imagenet1k-640p dataset hosted on Hugging Face and contributed by the HF Datasets community

ChristophSchuhmann/imagenet1k-by-SD-V1.4 dataset hosted on Hugging Face and contributed by the HF Datasets community

With the rapid development of social media，multimedia information on the internet is updated at an exponential rate. Obtaining and transmitting digital images have become convenient，considerably increasing the risk of malicious tampering and forgery of images. Accordingly，increasing attention is given to image authentication and content protection. Many image authentication schemes have emerged recently，such as watermarking，the use of digital signatures，and perceptual image hashing （PIH）. PIH，also known as image abstract or image fingerprint，is an effective technique for image authentication that has attracted widespread research attention in recent years. The goal of PIH is to authenticate an image by compressing perceptual robust features into a compact hash sequence with a fixed length. However，a general dataset in this field is lacking，and the dataset constructed using other methods have many problems. On the one hand，the types of image content-preserving manipulations used in these datasets are few and the intensity of attacks is rela⁃ tively weak. On the other hand，the distinct images used in these datasets are extremely different from the images that must be authenticated，making it easy to distinguish them from each other. The convolutional neural networks （CNNs） trained by these datasets have poor generalizability and can hardly cope with the complex and diverse image editing operations in reality. This important factor has limited the development of the PIH field. On the basis of the preceding knowl⁃ edge，we propose a specialized dataset based on various manipulations in this study. This dataset can deal with complex image authentication scenarios. The proposed dataset is divided into three subsets：original，perceptual identical，and perceptual distinct images. The latter two correspond to the robustness and discrimination of PIH，respectively. Original images are selected from ImageNet1K，and each of them corresponds to one category. For identical images，we summarize the content-preserving manipulations commonly used in the field of PIH and group them into four major categories： geomet⁃ ric，enhancement，filter，and editing manipulations. Each major category is subdivided into different types， for a total of 35 single-image content-preserving manipulations. To ensure the diversity and reflect the randomness of image editing in reality，we set a threshold for each type of image content-preserving manipulation and let them randomly select the attack intensity within this range. In addition，we randomly combine multiple single-image content-preserving manipulations to form combination manipulations. Some combined manipulations in the test set have not been learned in the training set due to the randomness. This result is also in line with practical application scenarios，because many unlearned，combined image editing manipulations exist in reality. For perceptual distinct images， except for a portion of images unrelated to the original images，the other portions are selected from the same category that corresponds to each original image，increasing the difficulty of the dataset and improving the generalizability of the trained CNNs. Compared with previously adopted datasets，our dataset conforms more to the actual application scenario of the PIH task. Our dataset contains 1 200 original images，and each original image is subjected to 48 image content-preserving manipulations to generate 48 perceptual identical images. To balance the number of perceptual identical and distinct images，we also select 48 perceptual distinct images for each original image. Then，24 images are randomly selected among them，and the other 24 images are semantically similar to the original images. Therefore，each batch contains 1 original image，48 perceptual identical images，and 48 perceptual distinct images，for a total of 97 images. Our dataset has 1 200 original images or 116 400 images in total. The large amount of data ensures the effective training of CNNs.

sdtana/imagenet1k-256 dataset hosted on Hugging Face and contributed by the HF Datasets community

zenless-lab/imagenet1k-640p dataset hosted on Hugging Face and contributed by the HF Datasets community

LeeWlving/ImageNet1K-Fool-1000 dataset hosted on Hugging Face and contributed by the HF Datasets community

fzhu22/imagenet1k-clip-preproc dataset hosted on Hugging Face and contributed by the HF Datasets community

fzhu22/imagenet1k-vit-preproc-3 dataset hosted on Hugging Face and contributed by the HF Datasets community