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Datasets/ImageNet

ImageNet

ImagesCustom (research, non-commercial)Introduced 2009-01-01

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

Total number of non-empty WordNet synsets: 21841
Total number of images: 14197122
Number of images with bounding box annotations: 1,034,908
Number of synsets with SIFT features: 1000
Number of images with SIFT features: 1.2 million

Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

Benchmarks

1 Image, 2*2 Stitchi/FID 1 Image, 2*2 Stitchi/PSNR 1 Image, 2*2 Stitchi/SSIM 10-shot image generation/FID 10-shot image generation/PSNR 10-shot image generation/SSIM 16k/MAP 16k/FID 16k/PSNR 16k/SSIM 2D Classification/MAP 2D Object Detection/MAP 2D Semantic Segmentation/GFLOPs 3D/MAP 3D Object Super-Resolution/FID 3D Object Super-Resolution/PSNR 3D Object Super-Resolution/SSIM Adversarial Defense/Accuracy Adversarial Robustness/Accuracy AutoML/Top-1 Error Rate AutoML/Accuracy AutoML/Params AutoML/MACs AutoML/FLOPs Classification/GFLOPs Classification/Top 1 Accuracy Composed Image Retrieval (CoIR)/Average Recall Data Augmentation/Accuracy (%)Image Classification/Top 1 Accuracy Image Classification/Number of params Image Classification/GFLOPs Image Classification/Hardware Burden Image Classification/Top 5 Accuracy Image Classification/Operations per network pass Image Classification/Number of Params Image Classification/Accuracy (%)Image Classification/ARI Image Clustering/Accuracy Image Clustering/NMI Image Clustering/ARI Image Colorization/Consistency Image Colorization/FID Image Deblurring/FID Image Deblurring/PSNR Image Deblurring/SSIM Image Generation/FID Image Generation/PSNR Image Generation/SSIM Image Inpainting/FID Image Inpainting/PSNR Image Inpainting/SSIM Image Reconstruction/FID Image Reconstruction/LPIPS Image Reconstruction/PSNR Image Reconstruction/SSIM Image Retrieval/Average Recall Image Segmentation/GFLOPs Image Super-Resolution/FID Image Super-Resolution/PSNR Image Super-Resolution/SSIM JPEG Decompression/FID-5K JPEG Decompression/IS JPEG Decompression/CA JPEG Decompression/PD Knowledge Distillation/Top-1 accuracy %Knowledge Distillation/model size Knowledge Distillation/CRD training setting Medical Image Classification/GFLOPs Medical Image Classification/Top 1 Accuracy Model Compression/Top-1 Network Pruning/Accuracy Network Pruning/GFLOPs Network Pruning/MParams Neural Architecture Search/Top-1 Error Rate Neural Architecture Search/Accuracy Neural Architecture Search/Params Neural Architecture Search/MACs Neural Architecture Search/FLOPs Object Detection/MAP Object Localization/GT-known localization accuracy Object Localization/Top-1 Localization Accuracy Object Localization/average top-1 classification accuracy Prompt Engineering/Harmonic mean Quantization/Top-1 Accuracy (%)Quantization/Weight bits Quantization/Activation bits Representation Learning/ADCC Representation Learning/Average Drop Representation Learning/Average Increase Sparse Learning/Top-1 Accuracy Super-Resolution/FID Super-Resolution/PSNR Super-Resolution/SSIM Visual Question Answering (VQA)/ClipMatch@1 Visual Question Answering (VQA)/ClipMatch@5 Visual Question Answering (VQA)/Contains Visual Question Answering (VQA)/ExactMatch Visual Question Answering (VQA)/Follow-up ClipMatch@1 Visual Question Answering (VQA)/Follow-up ClipMatch@5 Visual Question Answering (VQA)/Follow-up Contains Visual Question Answering (VQA)/Follow-up ExactMatch Zero-Shot Learning/Top 1 Accuracy Zero-Shot Transfer Image Classification/Param Zero-Shot Transfer Image Classification/Accuracy (Private)Zero-Shot Transfer Image Classification/Accuracy (Public)

Related Benchmarks

ImageNet (1-shot)/Few-Shot Image Classification/Top-5 Accuracy ImageNet (1-shot)/Image Classification/Top-5 Accuracy ImageNet (Fine-grained 6 Tasks)/Continual Learning/Accuracy ImageNet (finetuned)/Image Classification/Number of Params ImageNet (finetuned)/Image Classification/Top 1 Accuracy ImageNet (non-targeted PGD, max perturbation=4)/Adversarial Defense/Accuracy ImageNet (targeted PGD, max perturbation=16)/Adversarial Defense/Accuracy ImageNet - 0-Shot/Few-Shot Image Classification/Accuracy ImageNet - 0-Shot/Image Classification/Accuracy ImageNet - 0.2% labeled data/Image Classification/ImageNet Top-1 Accuracy ImageNet - 0.2% labeled data/Semi-Supervised Image Classification/ImageNet Top-1 Accuracy ImageNet - 1% labeled data/Image Classification/Number of params ImageNet - 1% labeled data/Image Classification/Top 1 Accuracy ImageNet - 1% labeled data/Image Classification/Top 5 Accuracy ImageNet - 1% labeled data/Semi-Supervised Image Classification/Number of params ImageNet - 1% labeled data/Semi-Supervised Image Classification/Top 1 Accuracy ImageNet - 1% labeled data/Semi-Supervised Image Classification/Top 5 Accuracy ImageNet - 1-shot/Few-Shot Image Classification/Top 1 Accuracy ImageNet - 1-shot/Image Classification/Top 1 Accuracy ImageNet - 10 steps/Incremental Learning/# M Params ImageNet - 10 steps/Incremental Learning/Average Incremental Accuracy ImageNet - 10 steps/Incremental Learning/Average Incremental Accuracy Top-5 ImageNet - 10 steps/Incremental Learning/Final Accuracy ImageNet - 10 steps/Incremental Learning/Final Accuracy Top-5 ImageNet - 10% labeled data/Image Classification/Number of params ImageNet - 10% labeled data/Image Classification/Top 1 Accuracy ImageNet - 10% labeled data/Image Classification/Top 5 Accuracy ImageNet - 10% labeled data/Semi-Supervised Image Classification/Number of params ImageNet - 10% labeled data/Semi-Supervised Image Classification/Top 1 Accuracy ImageNet - 10% labeled data/Semi-Supervised Image Classification/Top 5 Accuracy ImageNet - 10-shot/Few-Shot Image Classification/Top 1 Accuracy ImageNet - 10-shot/Image Classification/Top 1 Accuracy ImageNet - 5-shot/Few-Shot Image Classification/Top 1 Accuracy ImageNet - 5-shot/Image Classification/Top 1 Accuracy ImageNet - 500 classes + 10 steps of 50 classes/Incremental Learning/Average Incremental Accuracy ImageNet - 500 classes + 10 steps of 50 classes/Incremental Learning/Final Accuracy ImageNet - 500 classes + 25 steps of 20 classes/Incremental Learning/Average Incremental Accuracy ImageNet - 500 classes + 5 steps of 100 classes/Incremental Learning/Average Incremental Accuracy ImageNet - 500 classes + 5 steps of 100 classes/Incremental Learning/Final Accuracy ImageNet - ResNet 50 - 90% sparsity/Network Pruning/Top-1 Accuracy ImageNet 128x128/Conditional Image Generation/FID ImageNet 128x128/Conditional Image Generation/Inception score ImageNet 128x128/Image Generation/FID ImageNet 128x128/Image Generation/IS ImageNet 128x128/Image Generation/Inception score ImageNet 128x128/Image Generation/Precision ImageNet 128x128/Image Generation/Recall ImageNet 256x256/Conditional Image Generation/FID ImageNet 256x256/Conditional Image Generation/Inception score ImageNet 256x256/Image Generation/FID ImageNet 256x256/Image Generation/Inception score ImageNet 256x256/Image Generation/NFE ImageNet 256x256/Image Reconstruction/FID ImageNet 256x256 - 1 labeled data per class/Image Generation/FID-50k ImageNet 256x256 - 1 labeled data per class/Image Generation/IS ImageNet 256x256 - 1 labeled data per class/Image Generation/Precision ImageNet 256x256 - 1 labeled data per class/Image Generation/Recall ImageNet 256x256 - 1 labeled data per class/Image Generation/sFID ImageNet 256x256 - 1% labeled data/Image Generation/FID-50k ImageNet 256x256 - 1% labeled data/Image Generation/IS ImageNet 256x256 - 1% labeled data/Image Generation/Precision ImageNet 256x256 - 1% labeled data/Image Generation/Recall ImageNet 256x256 - 1% labeled data/Image Generation/sFID ImageNet 256x256 - 2 labeled data per class/Image Generation/FID-50k ImageNet 256x256 - 2 labeled data per class/Image Generation/IS ImageNet 256x256 - 2 labeled data per class/Image Generation/Precision ImageNet 256x256 - 2 labeled data per class/Image Generation/Recall ImageNet 256x256 - 2 labeled data per class/Image Generation/sFID ImageNet 256x256 - 5 labeled data per class/Image Generation/FID-50k ImageNet 256x256 - 5 labeled data per class/Image Generation/IS ImageNet 256x256 - 5 labeled data per class/Image Generation/Precision ImageNet 256x256 - 5 labeled data per class/Image Generation/Recall ImageNet 256x256 - 5 labeled data per class/Image Generation/sFID ImageNet 32x32/Density Estimation/NLL (bits/dim)ImageNet 32x32/Image Generation/FID ImageNet 32x32/Image Generation/Inception score ImageNet 32x32/Image Generation/bpd ImageNet 50 samples per class/Image Classification/1:1 Accuracy ImageNet 512x512/Image Generation/FID ImageNet 512x512/Image Generation/Inception score ImageNet 512x512/Image Generation/NFE ImageNet 64x64/Conditional Image Generation/FID ImageNet 64x64/Conditional Image Generation/Inception score ImageNet 64x64/Density Estimation/Log-likelihood ImageNet 64x64/Image Generation/Bits per dim ImageNet 64x64/Image Generation/FID ImageNet 64x64/Image Generation/Inception Score ImageNet 64x64/Image Generation/Inception score ImageNet 64x64/Image Generation/KID ImageNet 64x64/Image Generation/NFE ImageNet C-OOD (class-out-of-distribution)/Classification/Detection AUROC (severity 0)ImageNet C-OOD (class-out-of-distribution)/Classification/Detection AUROC (severity 10)ImageNet C-OOD (class-out-of-distribution)/Classification/Detection AUROC (severity 5)ImageNet Detection/16k/mAP ImageNet Detection/2D Classification/mAP ImageNet Detection/2D Object Detection/mAP ImageNet Detection/3D/mAP ImageNet Detection/Object Detection/mAP ImageNet ReaL/Image Classification/Accuracy ImageNet ReaL/Image Classification/Number of params ImageNet ReaL/Image Classification/Params ImageNet ReaL/Image Classification/Top 1 Accuracy ImageNet ReaL/Zero-Shot Transfer Image Classification/Accuracy (Private)ImageNet ReaL/Zero-Shot Transfer Image Classification/Accuracy (Public)ImageNet ResNet-50 - 50 Epochs/Stochastic Optimization/Top 1 Accuracy ImageNet ResNet-50 - 50 Epochs/Stochastic Optimization/Top 5 Accuracy ImageNet ResNet-50 - 60 Epochs/Stochastic Optimization/Top 1 Accuracy ImageNet ResNet-50 - 60 Epochs/Stochastic Optimization/Top 5 Accuracy ImageNet ResNet-50 - 90 Epochs/Stochastic Optimization/Top 1 Accuracy ImageNet V2/Image Classification/Top 1 Accuracy ImageNet V2/Prompt Engineering/Top-1 accuracy %ImageNet V2/Zero-Shot Transfer Image Classification/Accuracy (Private)ImageNet V2/Zero-Shot Transfer Image Classification/Accuracy (Public)ImageNet VID/16k/MAP ImageNet VID/2D Classification/MAP ImageNet VID/2D Object Detection/MAP ImageNet VID/3D/MAP ImageNet VID/Object Detection/MAP ImageNet ctest10k/Colorization/FID ImageNet ctest10k/Colorization/PSNR@1 ImageNet ctest10k/Colorization/PSNR@10 ImageNet ctest10k/Colorization/PSNR@100 ImageNet dogs vs ImageNet non-dogs/Out-of-Distribution Detection/AUROC ImageNet sigma100/3D Architecture/LPIPS ImageNet sigma100/3D Architecture/PSNR ImageNet sigma100/3D Architecture/SSIM ImageNet sigma100/Denoising/LPIPS ImageNet sigma100/Denoising/PSNR ImageNet sigma100/Denoising/SSIM ImageNet sigma150/3D Architecture/LPIPS ImageNet sigma150/3D Architecture/PSNR ImageNet sigma150/3D Architecture/SSIM ImageNet sigma150/Denoising/LPIPS ImageNet sigma150/Denoising/PSNR ImageNet sigma150/Denoising/SSIM ImageNet sigma200/3D Architecture/LPIPS ImageNet sigma200/3D Architecture/PSNR ImageNet sigma200/3D Architecture/SSIM ImageNet sigma200/Denoising/LPIPS ImageNet sigma200/Denoising/PSNR ImageNet sigma200/Denoising/SSIM ImageNet sigma250/3D Architecture/LPIPS ImageNet sigma250/3D Architecture/PSNR ImageNet sigma250/3D Architecture/SSIM ImageNet sigma250/Denoising/LPIPS ImageNet sigma250/Denoising/PSNR ImageNet sigma250/Denoising/SSIM ImageNet sigma50/3D Architecture/LPIPS ImageNet sigma50/3D Architecture/PSNR ImageNet sigma50/3D Architecture/SSIM ImageNet sigma50/Denoising/LPIPS ImageNet sigma50/Denoising/PSNR ImageNet sigma50/Denoising/SSIM ImageNet val/Colorization/FID-5K ImageNet-10/Image Classification/ARI ImageNet-10/Image Classification/Top 1 Accuracy ImageNet-10/Image Clustering/ARI ImageNet-10/Image Clustering/Accuracy ImageNet-10/Image Clustering/Backbone ImageNet-10/Image Clustering/Image Size ImageNet-10/Image Clustering/NMI ImageNet-100 (Class-IL, 5T)/Image Classification/Top 1 Accuracy ImageNet-100 (TEMI Split)/Image Classification/All accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Image Classification/All accuracy (50% Labeled)ImageNet-100 (TEMI Split)/Image Classification/Novel accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Image Classification/Novel accuracy (50% Labeled)ImageNet-100 (TEMI Split)/Image Classification/Params ImageNet-100 (TEMI Split)/Image Classification/Percentage correct ImageNet-100 (TEMI Split)/Image Classification/Seen accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Image Classification/Seen accuracy (50% Labeled)ImageNet-100 (TEMI Split)/Image Clustering/ACCURACY ImageNet-100 (TEMI Split)/Image Clustering/ARI ImageNet-100 (TEMI Split)/Image Clustering/NMI ImageNet-100 (TEMI Split)/Self-Supervised Learning/Top-1 Accuracy ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/All accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/All accuracy (50% Labeled)ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/Novel accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/Novel accuracy (50% Labeled)ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/Seen accuracy (10% Labeled)ImageNet-100 (TEMI Split)/Semi-Supervised Image Classification/Seen accuracy (50% Labeled)ImageNet-100 - 50 classes + 10 steps of 5 classes/Incremental Learning/Average Incremental Accuracy ImageNet-100 - 50 classes + 25 steps of 2 classes/Incremental Learning/Average Incremental Accuracy ImageNet-100 - 50 classes + 5 steps of 10 classes/Incremental Learning/Average Incremental Accuracy ImageNet-100 - 50 classes + 5 steps of 10 classes/Object Localization/Average Top-1 localization accuracy ImageNet-100 - 50 classes + 50 steps of 1 class/Incremental Learning/Average Incremental Accuracy ImageNet-10k - 5225 classes + 5 steps of 1045 classes/Incremental Learning/Final Accuracy ImageNet-1K (With LV-ViT-S)/Image Classification/GFLOPs ImageNet-1K (With LV-ViT-S)/Image Classification/Top 1 Accuracy ImageNet-1K (with DeiT-S)/Image Classification/GFLOPs ImageNet-1K (with DeiT-S)/Image Classification/Top 1 Accuracy ImageNet-1K (with DeiT-T)/Image Classification/GFLOPs ImageNet-1K (with DeiT-T)/Image Classification/Top 1 Accuracy ImageNet-1K vs ImageNet-C/Out-of-Distribution Detection/AUROC ImageNet-1K vs ImageNet-C/Out-of-Distribution Detection/FPR95 ImageNet-1K vs ImageNet-C/Out-of-Distribution Detection/Latency, ms ImageNet-1K vs ImageNet-O/Out-of-Distribution Detection/AUROC ImageNet-1K vs ImageNet-O/Out-of-Distribution Detection/FPR95 ImageNet-1K vs SSB-hard/Out-of-Distribution Detection/AUROC ImageNet-1K vs SSB-hard/Out-of-Distribution Detection/FPR95 ImageNet-1K vs SSB-hard/Out-of-Distribution Detection/Latency, ms ImageNet-1k to MSCOCO/Zero-Shot Learning/mAP ImageNet-1k vs Curated OODs (avg.)/Out-of-Distribution Detection/AUROC ImageNet-1k vs Curated OODs (avg.)/Out-of-Distribution Detection/FPR95 ImageNet-1k vs NINCO/Out-of-Distribution Detection/AUROC ImageNet-1k vs NINCO/Out-of-Distribution Detection/FPR@95 ImageNet-1k vs NINCO/Out-of-Distribution Detection/Latency, ms ImageNet-1k vs OpenImage-O/Out-of-Distribution Detection/AUROC ImageNet-1k vs OpenImage-O/Out-of-Distribution Detection/FPR95 ImageNet-1k vs OpenImage-O/Out-of-Distribution Detection/Latency, ms ImageNet-1k vs Places/Out-of-Distribution Detection/AUROC ImageNet-1k vs Places/Out-of-Distribution Detection/FPR95 ImageNet-1k vs SUN/Out-of-Distribution Detection/AUROC ImageNet-1k vs SUN/Out-of-Distribution Detection/FPR95 ImageNet-1k vs Textures/Out-of-Distribution Detection/AUROC ImageNet-1k vs Textures/Out-of-Distribution Detection/FPR95 ImageNet-1k vs Textures/Out-of-Distribution Detection/Latency, ms ImageNet-1k vs iNaturalist/Out-of-Distribution Detection/AUROC ImageNet-1k vs iNaturalist/Out-of-Distribution Detection/FPR95 ImageNet-1k vs iNaturalist/Out-of-Distribution Detection/Latency, ms ImageNet-200/Image Clustering/ ACCURACY ImageNet-200/Image Clustering/ARI ImageNet-200/Image Clustering/NMI ImageNet-21k/Prompt Engineering/Accuracy ImageNet-32/Image Classification/Top 1 Error ImageNet-50 (5 tasks) /Continual Learning/Accuracy ImageNet-50 (TEMI Split)/Image Clustering/ACCURACY ImageNet-50 (TEMI Split)/Image Clustering/ARI ImageNet-50 (TEMI Split)/Image Clustering/NMI ImageNet-64/Image Classification/Top 1 Error ImageNet-9/Image Classification/Top 1 Accuracy ImageNet-A/Adversarial Robustness/Accuracy ImageNet-A/Domain Adaptation/Number of params ImageNet-A/Domain Adaptation/Top 1 Error ImageNet-A/Domain Adaptation/Top-1 accuracy %ImageNet-A/Domain Generalization/Number of params ImageNet-A/Domain Generalization/Top-1 accuracy %ImageNet-A/Prompt Engineering/Top-1 accuracy %ImageNet-A/Unsupervised Domain Adaptation/Top 1 Error ImageNet-A/Zero-Shot Transfer Image Classification/Accuracy (Private)ImageNet-A/Zero-Shot Transfer Image Classification/Accuracy (Public)ImageNet-C/Adversarial Robustness/mean Corruption Error (mCE)ImageNet-C/Domain Adaptation/Mean Accuracy ImageNet-C/Domain Adaptation/Number of params ImageNet-C/Domain Adaptation/Top 1 Accuracy ImageNet-C/Domain Adaptation/mean Corruption Error (mCE)ImageNet-C/Domain Generalization/Number of params ImageNet-C/Domain Generalization/Top 1 Accuracy ImageNet-C/Domain Generalization/mean Corruption Error (mCE)ImageNet-C/Unsupervised Domain Adaptation/mean Corruption Error (mCE)ImageNet-Caltech/Domain Adaptation/Accuracy (%)ImageNet-FS (1-shot, all)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (1-shot, all)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (1-shot, novel)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (1-shot, novel)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (10-shot, all)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (10-shot, all)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (10-shot, novel)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (10-shot, novel)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (2-shot, all)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (2-shot, all)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (2-shot, novel)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (2-shot, novel)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (5-shot, all)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (5-shot, all)/Image Classification/Top-5 Accuracy (%)ImageNet-FS (5-shot, novel)/Few-Shot Image Classification/Top-5 Accuracy (%)ImageNet-FS (5-shot, novel)/Image Classification/Top-5 Accuracy (%)ImageNet-GLT/Few-Shot Image Classification/Accuracy ImageNet-GLT/Generalized Few-Shot Classification/Accuracy ImageNet-GLT/Generalized Few-Shot Learning/Accuracy ImageNet-GLT/Image Classification/Accuracy ImageNet-GLT/Long-tail Learning/Accuracy ImageNet-Hard/Image Classification/Accuracy (%)ImageNet-LT/Conditional Image Generation/FID ImageNet-LT/Few-Shot Image Classification/Top-1 Accuracy ImageNet-LT/Generalized Few-Shot Classification/Top-1 Accuracy ImageNet-LT/Generalized Few-Shot Learning/Top-1 Accuracy ImageNet-LT/Image Classification/Top-1 Accuracy ImageNet-LT/Image Generation/FID ImageNet-LT/Long-tail Learning/Top-1 Accuracy ImageNet-LT-d/Few-Shot Image Classification/Per-Class Accuracy ImageNet-LT-d/Generalized Few-Shot Classification/Per-Class Accuracy ImageNet-LT-d/Generalized Few-Shot Learning/Per-Class Accuracy ImageNet-LT-d/Image Classification/Per-Class Accuracy ImageNet-LT-d/Long-tail Learning/Per-Class Accuracy ImageNet-P/Image Classification/Top 5 Accuracy ImageNet-R/Composed Image Retrieval (CoIR)/(Recall@10+Recall@50)/2 ImageNet-R/Composed Image Retrieval (CoIR)/mAP ImageNet-R/Domain Adaptation/Top 1 Error ImageNet-R/Domain Adaptation/Top-1 Error Rate ImageNet-R/Domain Generalization/Top-1 Error Rate ImageNet-R/Image Retrieval/(Recall@10+Recall@50)/2 ImageNet-R/Image Retrieval/mAP ImageNet-R/Prompt Engineering/Top-1 accuracy %ImageNet-R/Unsupervised Domain Adaptation/Top 1 Error ImageNet-R/Zero-Shot Transfer Image Classification/Accuracy ImageNet-S/10-shot image generation/mIoU (test)ImageNet-S/10-shot image generation/mIoU (val)ImageNet-S/Prompt Engineering/Top-1 accuracy %ImageNet-S/Semantic Segmentation/mIoU (test)ImageNet-S/Semantic Segmentation/mIoU (val)ImageNet-S/Unsupervised Semantic Segmentation/mIoU (test)ImageNet-S/Unsupervised Semantic Segmentation/mIoU (val)ImageNet-S/Zero-Shot Transfer Image Classification/Accuracy (Private)ImageNet-S/Zero-Shot Transfer Image Classification/Top 5 Accuracy ImageNet-S-300/10-shot image generation/mIoU (test)ImageNet-S-300/10-shot image generation/mIoU (val)ImageNet-S-300/Semantic Segmentation/mIoU (test)ImageNet-S-300/Semantic Segmentation/mIoU (val)ImageNet-S-300/Unsupervised Semantic Segmentation/mIoU (test)ImageNet-S-300/Unsupervised Semantic Segmentation/mIoU (val)ImageNet-S-50/10-shot image generation/mIoU (test)ImageNet-S-50/10-shot image generation/mIoU (val)ImageNet-S-50/Semantic Segmentation/mIoU (test)ImageNet-S-50/Semantic Segmentation/mIoU (val)ImageNet-S-50/Unsupervised Semantic Segmentation/mIoU (test)ImageNet-S-50/Unsupervised Semantic Segmentation/mIoU (val)ImageNet-Sketch/Domain Adaptation/Top-1 accuracy ImageNet-Sketch/Domain Generalization/Top-1 accuracy ImageNet-Sketch/Image Classification/Accuracy ImageNet-Sketch/Zero-Shot Transfer Image Classification/Accuracy (Private)ImageNet-VidVRD/2D Semantic Segmentation/Recall@100 ImageNet-VidVRD/2D Semantic Segmentation/Recall@50 ImageNet-VidVRD/2D Semantic Segmentation/mAP ImageNet-VidVRD/Scene Parsing/Recall@100 ImageNet-VidVRD/Scene Parsing/Recall@50 ImageNet-VidVRD/Scene Parsing/mAP ImageNet-VidVRD/Scene Understanding/Recall@100 ImageNet-VidVRD/Scene Understanding/Recall@50 ImageNet-VidVRD/Scene Understanding/mAP ImageNet-VidVRD/Video scene graph generation/Recall@50 ImageNet-VidVRD/Visual Relationship Detection/Recall@100 ImageNet-VidVRD/Visual Relationship Detection/Recall@50 ImageNet-VidVRD/Visual Relationship Detection/mAP ImageNet100 - 10 steps/Incremental Learning/# M Params ImageNet100 - 10 steps/Incremental Learning/Average Incremental Accuracy ImageNet100 - 10 steps/Incremental Learning/Average Incremental Accuracy Top-5 ImageNet100 - 10 steps/Incremental Learning/Final Accuracy ImageNet100 - 10 steps/Incremental Learning/Final Accuracy Top-5 ImageNet100 - 20 steps/Incremental Learning/Average Incremental Accuracy ImageNet32/Classification/Recall@1 ImageNet32/Classification/Recall@10 ImageNet32/Classification/Recall@2 ImageNet32/Classification/Recall@5 ImageNet32/Image Compression/bpsp ImageNet32/Sparse Learning/Sparsity ImageNetSubset/Class Incremental Learning/Average accuracy - 5 tasks ImageNetSubset/Class Incremental Learning/average accuracy - 10 tasks ImageNetSubset/Class Incremental Learning/average accuracy - 20 tasks ImageNetSubset/Continual Learning/Average accuracy - 5 tasks ImageNetSubset/Continual Learning/average accuracy - 10 tasks ImageNetSubset/Continual Learning/average accuracy - 20 tasks ImageNet_CN/Zero-Shot Learning/Accuracy Imagenet-dog-15/Image Clustering/ARI Imagenet-dog-15/Image Clustering/Accuracy Imagenet-dog-15/Image Clustering/Backbone Imagenet-dog-15/Image Clustering/Image Size Imagenet-dog-15/Image Clustering/NMI Imagenette/Image Classification/Accuracy Imagenette, 100 Labels/Image Classification/Percentage error Imagenette, 100 Labels/Semi-Supervised Image Classification/Percentage error Imagenette, 20 Labels/Image Classification/Percentage error Imagenette, 20 Labels/Semi-Supervised Image Classification/Percentage error imagenet-1k/Contrastive Learning/ImageNet Top-1 Accuracy imagenet-1k/Image Classification/Top 1 Accuracy imagenet-1k/Image Clustering/ARI imagenet-1k/Image Clustering/Accuracy imagenet-1k/Image Clustering/NMI

Statistics

Papers: 15,430
Benchmarks: 105

Links

Tasks

1 Image, 2*2 Stitchi 10-shot image generation 16k 2D Classification 2D Object Detection 2D Semantic Segmentation 3D 3D Object Super-Resolution Adversarial Defense Adversarial Robustness AutoML Binarization Classification Classification with Binary Neural Network Color Image Denoising Composed Image Retrieval (CoIR)Contrastive Learning Data Augmentation Feature Upsampling Few-Shot Image Classification Few-Shot Learning Image Classification Image Classification with Differential Privacy Image Clustering Image Colorization Image Compressed Sensing Image Deblurring Image Generation Image Inpainting Image Reconstruction Image Retrieval Image Segmentation Image Super-Resolution JPEG Decompression Knowledge Distillation Medical Image Classification Model Compression Network Pruning Neural Architecture Search Object Detection Object Localization Object Recognition Prompt Engineering Quantization Representation Learning Self-Supervised Image Classification Semi-Supervised Image Classification Sparse Learning Super-Resolution Transductive Zero-Shot Classification Unsupervised Image Classification Visual Question Answering (VQA)Weakly Supervised Object Detection Weakly-Supervised Object Localization Zero-Shot Composed Image Retrieval (ZS-CIR)Zero-Shot Learning Zero-Shot Transfer Image Classification Zero-Shot Transfer Image Classification (CN)