Machine Learning Datasets | Papers With Code

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<span class=" icon-wrapper icon-fa icon-fa-solid" data-name="th"><svg viewBox="0 0 512 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M149.333 57.998v80c0 13.255-10.745 24-24 24H24c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24h101.333c13.255 0 24 10.745 24 24zm181.334 240c0 13.255-10.745 24-24.001 24H205.333c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24h101.334c13.255 0 24 10.745 24 24v80zm32-240c0-13.255 10.745-24 24-24H488c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H386.667c-13.255 0-24-10.745-24-24v-80zm-32 80c0 13.255-10.745 24-24.001 24H205.333c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24h101.334c13.255 0 24 10.745 24 24v80zm-205.334 56c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H24c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24h101.333zM0 377.998c0-13.255 10.745-24 24-24h101.333c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H24c-13.255 0-24-10.745-24-24v-80zm386.667-56c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24H488c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H386.667zm0 160c-13.255 0-24-10.745-24-24v-80c0-13.255 10.745-24 24-24H488c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H386.667zm-205.334-104c0-13.255 10.745-24 24-24h101.333c13.255 0 24 10.745 24 24v80c0 13.255-10.745 24-24 24H205.333c-13.255 0-24-10.745-24-24v-80z"/></svg></span><span class="d-md-none"> Gallery</span> </input> </label> </div> </div> <select class="custom-select custom-select-sm sort-order" name="o" onchange="this.form.submit()"> <option selected value="match">Best match</option> <option value="cited">Most cited</option> <option value="newest">Newest</option> </select> </form> <div id="id_datasets_filters" class="collapse collapse-md-show"> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name"> Filter by Modality </div> <div class="filter-items"> <a class="filter-item" href="?mod=images&page=1"> Images <span class="badge badge-light">2975</span> </a> <a class="filter-item" href="?mod=texts&page=1"> Texts <span class="badge badge-light">2863</span> </a> <a class="filter-item" href="?mod=videos&page=1"> Videos <span class="badge badge-light">939</span> </a> <a class="filter-item" href="?mod=audio&page=1"> Audio <span class="badge badge-light">450</span> </a> <a class="filter-item" href="?mod=medical&page=1"> Medical <span class="badge badge-light">368</span> </a> <a class="filter-item" href="?mod=3d&page=1"> 3D <span class="badge badge-light">348</span> </a> <a class="filter-item" href="?mod=graphs&page=1"> Graphs <span class="badge badge-light">257</span> </a> <a class="filter-item" href="?mod=time-series&page=1"> Time series <span class="badge badge-light">241</span> </a> <a class="filter-item" href="?mod=tabular&page=1"> Tabular <span class="badge badge-light">211</span> </a> <a class="filter-item" href="?mod=speech&page=1"> Speech <span class="badge badge-light">186</span> </a> <a class="filter-item" href="?mod=rgb-d&page=1"> RGB-D <span class="badge badge-light">177</span> </a> <a class="filter-item" href="?mod=environment&page=1"> Environment <span class="badge badge-light">137</span> </a> <a class="filter-item" href="?mod=point-cloud&page=1"> Point cloud <span class="badge badge-light">124</span> </a> <a class="filter-item" href="?mod=biomedical&page=1"> Biomedical <span class="badge badge-light">112</span> </a> <a class="filter-item" href="?mod=lidar&page=1"> LiDAR <span class="badge badge-light">85</span> </a> <a class="filter-item" href="?mod=rgb-video&page=1"> RGB Video <span class="badge badge-light">81</span> </a> <a class="filter-item" href="?mod=tracking&page=1"> Tracking <span class="badge badge-light">67</span> </a> <a class="filter-item" href="?mod=3d-meshes&page=1"> 3d meshes <span class="badge badge-light">58</span> </a> <a class="filter-item" href="?mod=biology&page=1"> Biology <span class="badge badge-light">58</span> </a> <a class="filter-item" href="?mod=actions&page=1"> Actions <span class="badge badge-light">55</span> </a> <a class="filter-item" href="?mod=tables&page=1"> Tables <span class="badge badge-light">46</span> </a> <a class="filter-item" href="?mod=stereo&page=1"> Stereo <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?mod=music&page=1"> Music <span class="badge badge-light">41</span> </a> <a class="filter-item" href="?mod=eeg&page=1"> EEG <span class="badge badge-light">40</span> </a> <a class="filter-item" href="?mod=hyperspectral-images&page=1"> Hyperspectral images <span class="badge badge-light">40</span> </a> <a class="filter-item" href="?mod=mri&page=1"> MRI <span class="badge badge-light">36</span> </a> <a class="filter-item" href="?mod=interactive&page=1"> Interactive <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?mod=physics&page=1"> Physics <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?mod=dialog&page=1"> Dialog <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?mod=midi&page=1"> Midi <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?mod=6d&page=1"> 6D <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?mod=ranking&page=1"> Ranking <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?mod=replay-data&page=1"> Replay data <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?mod=financial&page=1"> Financial <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=fmri&page=1"> fMRI <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?mod=cad&page=1"> Cad <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?mod=parallel&page=1"> Parallel <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?mod=lyrics&page=1"> Lyrics <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?mod=psg&page=1"> PSG <span class="badge badge-light">2</span> </a> </div> </div> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name">Filter by Task </div> <div class="filter-items"> <a class="filter-item" href="?task=question-answering&page=1"> Question Answering <span class="badge badge-light">379</span> </a> <a class="filter-item" href="?task=semantic-segmentation&page=1"> Semantic Segmentation <span class="badge badge-light">335</span> </a> <a class="filter-item" href="?task=object-detection&page=1"> Object Detection <span class="badge badge-light">293</span> </a> <a class="filter-item" href="?task=image-classification&page=1"> Image Classification <span class="badge badge-light">262</span> </a> <a class="filter-item" href="?task=classification-1&page=1"> Classification <span class="badge badge-light">188</span> </a> <a class="filter-item" href="?task=language-modelling&page=1"> Language Modelling <span class="badge badge-light">163</span> </a> <a class="filter-item" href="?task=text-classification&page=1"> Text Classification <span class="badge badge-light">150</span> </a> <a class="filter-item" href="?task=text-generation&page=1"> Text Generation <span class="badge badge-light">141</span> </a> <a class="filter-item" href="?task=visual-question-answering&page=1"> Visual Question Answering (VQA) <span class="badge badge-light">132</span> </a> <a class="filter-item" href="?task=named-entity-recognition-ner&page=1"> Named Entity Recognition (NER) <span class="badge badge-light">127</span> </a> <a class="filter-item" href="?task=pose-estimation&page=1"> Pose Estimation <span class="badge badge-light">120</span> </a> <a class="filter-item" href="?task=anomaly-detection&page=1"> Anomaly Detection <span class="badge badge-light">116</span> </a> <a class="filter-item" href="?task=action-recognition-in-videos&page=1"> Action Recognition <span class="badge badge-light">110</span> </a> <a class="filter-item" href="?task=sentiment-analysis&page=1"> Sentiment Analysis <span class="badge badge-light">99</span> </a> <a class="filter-item" href="?task=instance-segmentation&page=1"> Instance Segmentation <span class="badge badge-light">96</span> </a> <a class="filter-item" href="?task=reading-comprehension&page=1"> Reading Comprehension <span class="badge badge-light">95</span> </a> <a class="filter-item" href="?task=domain-adaptation&page=1"> Domain Adaptation <span class="badge badge-light">94</span> </a> <a class="filter-item" href="?task=text-summarization&page=1"> Text Summarization <span class="badge badge-light">92</span> </a> <a class="filter-item" href="?task=speech-recognition&page=1"> Speech Recognition <span class="badge badge-light">91</span> </a> <a class="filter-item" href="?task=information-retrieval&page=1"> Information Retrieval <span class="badge badge-light">88</span> </a> <a class="filter-item" href="?task=image-retrieval&page=1"> Image Retrieval <span class="badge badge-light">83</span> </a> <a class="filter-item" href="?task=machine-translation&page=1"> Machine Translation <span class="badge badge-light">81</span> </a> <a class="filter-item" href="?task=natural-language-inference&page=1"> Natural Language Inference <span class="badge badge-light">81</span> </a> <a class="filter-item" href="?task=relation-extraction&page=1"> Relation Extraction <span class="badge badge-light">77</span> </a> <a class="filter-item" href="?task=depth-estimation&page=1"> Depth Estimation <span class="badge badge-light">75</span> </a> <a class="filter-item" href="?task=image-captioning&page=1"> Image Captioning <span class="badge badge-light">72</span> </a> <a class="filter-item" href="?task=image-generation&page=1"> Image Generation <span class="badge badge-light">72</span> </a> <a class="filter-item" href="?task=node-classification&page=1"> Node Classification <span class="badge badge-light">72</span> </a> <a class="filter-item" href="?task=2d-object-detection&page=1"> 2D Object Detection <span class="badge badge-light">70</span> </a> <a class="filter-item" href="?task=2d-semantic-segmentation&page=1"> 2D Semantic Segmentation <span class="badge badge-light">70</span> </a> <a class="filter-item" href="?task=natural-language-understanding&page=1"> Natural Language Understanding <span class="badge badge-light">70</span> </a> <a class="filter-item" href="?task=autonomous-driving&page=1"> Autonomous Driving <span class="badge badge-light">67</span> </a> <a class="filter-item" href="?task=link-prediction&page=1"> Link Prediction <span class="badge badge-light">66</span> </a> <a class="filter-item" href="?task=face-recognition&page=1"> Face Recognition <span class="badge badge-light">65</span> </a> <a class="filter-item" href="?task=data-augmentation&page=1"> Data Augmentation <span class="badge badge-light">63</span> </a> <a class="filter-item" href="?task=person-re-identification&page=1"> Person Re-Identification <span class="badge badge-light">63</span> </a> <a class="filter-item" href="?task=object-tracking&page=1"> Object Tracking <span class="badge badge-light">62</span> </a> <a class="filter-item" href="?task=3d-object-detection&page=1"> 3D Object Detection <span class="badge badge-light">60</span> </a> <a class="filter-item" href="?task=multi-task-learning&page=1"> Multi-Task Learning <span class="badge badge-light">58</span> </a> <a class="filter-item" href="?task=3d-reconstruction&page=1"> 3D Reconstruction <span class="badge badge-light">57</span> </a> <a class="filter-item" href="?task=recommendation-systems&page=1"> Recommendation Systems <span class="badge badge-light">57</span> </a> <a class="filter-item" href="?task=common-sense-reasoning&page=1"> Common Sense Reasoning <span class="badge badge-light">56</span> </a> <a class="filter-item" href="?task=code-generation&page=1"> Code Generation <span class="badge badge-light">53</span> </a> <a class="filter-item" href="?task=graph-classification&page=1"> Graph Classification <span class="badge badge-light">52</span> </a> <a class="filter-item" href="?task=word-embeddings&page=1"> Word Embeddings <span class="badge badge-light">52</span> </a> <a class="filter-item" href="?task=abstractive-text-summarization&page=1"> Abstractive Text Summarization <span class="badge badge-light">50</span> </a> <a class="filter-item" href="?task=3d-human-pose-estimation&page=1"> 3D Human Pose Estimation <span class="badge badge-light">49</span> </a> <a class="filter-item" href="?task=emotion-recognition&page=1"> Emotion Recognition <span class="badge badge-light">48</span> </a> <a class="filter-item" href="?task=optical-character-recognition&page=1"> Optical Character Recognition (OCR) <span class="badge badge-light">48</span> </a> <a class="filter-item" href="?task=video-understanding&page=1"> Video Understanding <span class="badge badge-light">48</span> </a> <a class="filter-item" href="?task=medical-image-segmentation&page=1"> Medical Image Segmentation <span class="badge badge-light">46</span> </a> <a class="filter-item" href="?task=coreference-resolution&page=1"> Coreference Resolution <span class="badge badge-light">45</span> </a> <a class="filter-item" href="?task=few-shot-learning&page=1"> Few-Shot Learning <span class="badge badge-light">45</span> </a> <a class="filter-item" href="?task=semantic-parsing&page=1"> Semantic Parsing <span class="badge badge-light">45</span> </a> <a class="filter-item" href="?task=hate-speech-detection&page=1"> Hate Speech Detection <span class="badge badge-light">44</span> </a> <a class="filter-item" href="?task=knowledge-graphs&page=1"> Knowledge Graphs <span class="badge badge-light">44</span> </a> <a class="filter-item" href="?task=entity-linking&page=1"> Entity Linking <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=machine-reading-comprehension&page=1"> Machine Reading Comprehension <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=object-recognition&page=1"> Object Recognition <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=scene-understanding&page=1"> Scene Understanding <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=self-supervised-learning&page=1"> Self-Supervised Learning <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=visual-reasoning&page=1"> Visual Reasoning <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=zero-shot-learning&page=1"> Zero-Shot Learning <span class="badge badge-light">43</span> </a> <a class="filter-item" href="?task=image-clustering&page=1"> Image Clustering <span class="badge badge-light">42</span> </a> <a class="filter-item" href="?task=misinformation&page=1"> Misinformation <span class="badge badge-light">42</span> </a> <a class="filter-item" href="?task=action-recognition&page=1"> Temporal Action Localization <span class="badge badge-light">42</span> </a> <a class="filter-item" href="?task=image-super-resolution&page=1"> Image Super-Resolution <span class="badge badge-light">41</span> </a> <a class="filter-item" href="?task=decision-making&page=1"> Decision Making <span class="badge badge-light">39</span> </a> <a class="filter-item" href="?task=face-detection&page=1"> Face Detection <span class="badge badge-light">39</span> </a> <a class="filter-item" href="?task=video-question-answering&page=1"> Video Question Answering <span class="badge badge-light">39</span> </a> <a class="filter-item" href="?task=3d-semantic-segmentation&page=1"> 3D Semantic Segmentation <span class="badge badge-light">38</span> </a> <a class="filter-item" href="?task=audio-classification&page=1"> Audio Classification <span class="badge badge-light">38</span> </a> <a class="filter-item" href="?task=fine-grained-image-classification&page=1"> Fine-Grained Image Classification <span class="badge badge-light">38</span> </a> <a class="filter-item" href="?task=multi-object-tracking&page=1"> Multi-Object Tracking <span class="badge badge-light">37</span> </a> <a class="filter-item" href="?task=novel-view-synthesis&page=1"> Novel View Synthesis <span class="badge badge-light">37</span> </a> <a class="filter-item" href="?task=video-captioning&page=1"> Video Captioning <span class="badge badge-light">36</span> </a> <a class="filter-item" href="?task=3d-pose-estimation&page=1"> 3D Pose Estimation <span class="badge badge-light">35</span> </a> <a class="filter-item" href="?task=optical-flow-estimation&page=1"> Optical Flow Estimation <span class="badge badge-light">35</span> </a> <a class="filter-item" href="?task=trajectory-prediction&page=1"> Trajectory Prediction <span class="badge badge-light">35</span> </a> <a class="filter-item" href="?task=action-detection&page=1"> Action Detection <span class="badge badge-light">34</span> </a> <a class="filter-item" href="?task=image-segmentation&page=1"> Image Segmentation <span class="badge badge-light">34</span> </a> <a class="filter-item" href="?task=panoptic-segmentation&page=1"> Panoptic Segmentation <span class="badge badge-light">34</span> </a> <a class="filter-item" href="?task=continual-learning&page=1"> Continual Learning <span class="badge badge-light">33</span> </a> <a class="filter-item" href="?task=stance-detection&page=1"> Stance Detection <span class="badge badge-light">33</span> </a> <a class="filter-item" href="?task=dialogue-generation&page=1"> Dialogue Generation <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=monocular-depth-estimation&page=1"> Monocular Depth Estimation <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=retrieval&page=1"> Retrieval <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=time-series-forecasting&page=1"> Time Series Forecasting <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=unsupervised-domain-adaptation&page=1"> Unsupervised Domain Adaptation <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=video-retrieval&page=1"> Video Retrieval <span class="badge badge-light">32</span> </a> <a class="filter-item" href="?task=metric-learning&page=1"> Metric Learning <span class="badge badge-light">31</span> </a> <a class="filter-item" href="?task=action-classification&page=1"> Action Classification <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=activity-recognition&page=1"> Activity Recognition <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=image-to-image-translation&page=1"> Image-to-Image Translation <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=music-generation&page=1"> Music Generation <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=skeleton-based-action-recognition&page=1"> Skeleton Based Action Recognition <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=text-retrieval&page=1"> Text Retrieval <span class="badge badge-light">30</span> </a> <a class="filter-item" href="?task=automatic-speech-recognition&page=1"> Automatic Speech Recognition (ASR) <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=facial-expression-recognition&page=1"> Facial Expression Recognition (FER) <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=fake-news-detection&page=1"> Fake News Detection <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=multi-label-classification&page=1"> Multi-Label Classification <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=scene-text-recognition&page=1"> Scene Text Recognition <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=visual-question-answering-1&page=1"> Visual Question Answering <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?task=autonomous-vehicles&page=1"> Autonomous Vehicles <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?task=document-summarization&page=1"> Document Summarization <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?task=domain-generalization&page=1"> Domain Generalization <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?task=architecture-search&page=1"> Neural Architecture Search <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?task=visual-object-tracking&page=1"> Visual Object Tracking <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?task=visual-tracking&page=1"> Visual Tracking <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?task=drug-discovery&page=1"> Drug Discovery <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=object-counting&page=1"> Object Counting <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=open-domain-question-answering&page=1"> Open-Domain Question Answering <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=part-of-speech-tagging&page=1"> Part-Of-Speech Tagging <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=unsupervised-anomaly-detection&page=1"> Unsupervised Anomaly Detection <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=visual-odometry&page=1"> Visual Odometry <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?task=emotion-classification&page=1"> Emotion Classification <span class="badge badge-light">25</span> </a> <a class="filter-item" href="?task=cg&page=1"> NER <span class="badge badge-light">25</span> </a> <a class="filter-item" href="?task=sign-language-recognition&page=1"> Sign Language Recognition <span class="badge badge-light">25</span> </a> <a class="filter-item" href="?task=2d-human-pose-estimation&page=1"> 2D Human Pose Estimation <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=data-to-text-generation&page=1"> Data-to-Text Generation <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=face-verification&page=1"> Face Verification <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=few-shot-image-classification&page=1"> Few-Shot Image Classification <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=music-information-retrieval&page=1"> Music Information Retrieval <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=segmentation&page=1"> Segmentation <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=time-series&page=1"> Time Series Analysis <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=video-prediction&page=1"> Video Prediction <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?task=fairness&page=1"> Fairness <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=low-light-image-enhancement&page=1"> Low-Light Image Enhancement <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=out-of-distribution-detection&page=1"> Out-of-Distribution Detection <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=slot-filling&page=1"> Slot Filling <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=visual-localization&page=1"> Visual Localization <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=visual-place-recognition&page=1"> Visual Place Recognition <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?task=deepfake-detection&page=1"> DeepFake Detection <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=hand-pose-estimation&page=1"> Hand Pose Estimation <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=human-object-interaction-detection&page=1"> Human-Object Interaction Detection <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=math-word-problem-solving&page=1"> Math Word Problem Solving <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=question-generation&page=1"> Question Generation <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=relation-classification&page=1"> Relation Classification <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=scene-classification&page=1"> Scene Classification <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?task=cross-modal-retrieval&page=1"> Cross-Modal Retrieval <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=crowd-counting&page=1"> Crowd Counting <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=denoising&page=1"> Denoising <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=handwriting-recognition&page=1"> Handwriting Recognition <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=intent-detection&page=1"> Intent Detection <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=mathematical-reasoning&page=1"> Mathematical Reasoning <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=multimodal-deep-learning&page=1"> Multimodal Deep Learning <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=simultaneous-localization-and-mapping&page=1"> Simultaneous Localization and Mapping <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=speech-synthesis&page=1"> Speech Synthesis <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=super-resolution&page=1"> Super-Resolution <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?task=6d-pose-estimation-1&page=1"> 6D Pose Estimation <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=aspect-based-sentiment-analysis&page=1"> Aspect-Based Sentiment Analysis (ABSA) <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=image-manipulation-detection&page=1"> Image Manipulation Detection <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=molecular-property-prediction&page=1"> Molecular Property Prediction <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=robotic-grasping&page=1"> Robotic Grasping <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=sound-event-detection&page=1"> Sound Event Detection <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=speech-emotion-recognition&page=1"> Speech Emotion Recognition <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=speech-enhancement&page=1"> Speech Enhancement <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=style-transfer&page=1"> Style Transfer <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=text-to-image-generation&page=1"> Text-to-Image Generation <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=video-classification&page=1"> Video Classification <span class="badge badge-light">20</span> </a> <a class="filter-item" href="?task=3d-hand-pose-estimation&page=1"> 3D Hand Pose Estimation <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=age-estimation&page=1"> Age Estimation <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=cell-segmentation&page=1"> Cell Segmentation <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=graph-clustering&page=1"> Graph Clustering <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=image-denoising&page=1"> Image Denoising <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=imitation-learning&page=1"> Imitation Learning <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=stereo-matching-1&page=1"> Stereo Matching <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=task-oriented-dialogue-systems&page=1"> Task-Oriented Dialogue Systems <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=text-simplification&page=1"> Text Simplification <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=traffic-prediction&page=1"> Traffic Prediction <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=regression-1&page=1"> regression <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?task=graph-regression&page=1"> Graph Regression <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=language-identification&page=1"> Language Identification <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=medical-diagnosis&page=1"> Medical Diagnosis <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=meta-learning&page=1"> Meta-Learning <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=quantization&page=1"> Quantization <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=reinforcement-learning-1&page=1"> Reinforcement Learning (RL) <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=semantic-textual-similarity&page=1"> Semantic Textual Similarity <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?task=action-recognition-in-videos-2&page=1"> Action Recognition In Videos <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=action-segmentation&page=1"> Action Segmentation <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=animal-pose-estimation&page=1"> Animal Pose Estimation <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=beat-tracking&page=1"> Beat Tracking <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=binarization&page=1"> Binarization <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=fact-verification&page=1"> Fact Verification <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=lane-detection&page=1"> Lane Detection <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=logical-reasoning&page=1"> Logical Reasoning <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=motion-synthesis&page=1"> Motion Synthesis <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=motor-imagery-decoding-left-hand-vs-right&page=1"> Motor Imagery Decoding (left-hand vs right-hand) <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=paraphrase-identification&page=1"> Paraphrase Identification <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=smac-1&page=1"> SMAC+ <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=salient-object-detection-1&page=1"> Salient Object Detection <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=scene-text-detection&page=1"> Scene Text Detection <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=text-to-sql&page=1"> Text-To-SQL <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=text-to-speech-synthesis&page=1"> Text-To-Speech Synthesis <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=topic-models&page=1"> Topic Models <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=translation&page=1"> Translation <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=visual-navigation&page=1"> Visual Navigation <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?task=3d-instance-segmentation-1&page=1"> 3D Instance Segmentation <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=contrastive-learning&page=1"> Contrastive Learning <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=document-classification&page=1"> Document Classification <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=face-alignment&page=1"> Face Alignment <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=face-anti-spoofing&page=1"> Face Anti-Spoofing <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=grammatical-error-correction&page=1"> Grammatical Error Correction <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=image-enhancement&page=1"> Image Enhancement <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=image-inpainting&page=1"> Image Inpainting <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=knowledge-graph-completion&page=1"> Knowledge Graph Completion <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=long-tail-learning&page=1"> Long-tail Learning <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=multiple-object-tracking&page=1"> Multiple Object Tracking <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=object-localization&page=1"> Object Localization <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=paraphrase-generation&page=1"> Paraphrase Generation <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=prompt-engineering&page=1"> Prompt Engineering <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=salient-object-detection&page=1"> RGB Salient Object Detection <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=scene-recognition&page=1"> Scene Recognition <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=speech-separation&page=1"> Speech Separation <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=time-series-classification&page=1"> Time Series Classification <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=transfer-learning&page=1"> Transfer Learning <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=video-object-segmentation&page=1"> Video Object Segmentation <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=video-super-resolution&page=1"> Video Super-Resolution <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?task=3d-human-reconstruction&page=1"> 3D Human Reconstruction <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=active-learning&page=1"> Active Learning <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=change-detection&page=1"> Change Detection <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=computed-tomography-ct&page=1"> Computed Tomography (CT) <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=cross-lingual-transfer&page=1"> Cross-Lingual Transfer <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=dependency-parsing&page=1"> Dependency Parsing <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=facial-landmark-detection&page=1"> Facial Landmark Detection <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=gaze-estimation&page=1"> Gaze Estimation <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=hand-gesture-recognition&page=1"> Hand Gesture Recognition <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=image-dehazing&page=1"> Image Dehazing <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=learning-with-noisy-labels&page=1"> Learning with noisy labels <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=lesion-segmentation&page=1"> Lesion Segmentation <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=node-classification-on-non-homophilic&page=1"> Node Classification on Non-Homophilic (Heterophilic) Graphs <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=node-clustering&page=1"> Node Clustering <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=pedestrian-detection&page=1"> Pedestrian Detection <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=robot-navigation&page=1"> Robot Navigation <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=video-frame-interpolation&page=1"> Video Frame Interpolation <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=weather-forecasting&page=1"> Weather Forecasting <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=within-session-erp&page=1"> Within-Session ERP <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=zero-shot-text-search&page=1"> Zero-shot Text Search <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?task=3d-human-action-recognition&page=1"> 3D Action Recognition <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=audio-source-separation&page=1"> Audio Source Separation <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=automatic-speech-recognition-2&page=1"> Automatic Speech Recognition <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=code-search&page=1"> Code Search <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=deblurring&page=1"> Deblurring <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=event-extraction&page=1"> Event Extraction <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=gesture-recognition&page=1"> Gesture Recognition <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=instruction-following&page=1"> Instruction Following <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=joint-entity-and-relation-extraction&page=1"> Joint Entity and Relation Extraction <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=license-plate-recognition&page=1"> License Plate Recognition <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=multi-document-summarization&page=1"> Multi-Document Summarization <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=open-information-extraction&page=1"> Open Information Extraction <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=sarcasm-detection&page=1"> Sarcasm Detection <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=self-driving-cars&page=1"> Self-Driving Cars <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=semantic-role-labeling&page=1"> Semantic Role Labeling <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=semantic-similarity&page=1"> Semantic Similarity <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=sign-language-translation&page=1"> Sign Language Translation <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=spoken-language-understanding&page=1"> Spoken Language Understanding <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=trajectory-forecasting&page=1"> Trajectory Forecasting <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=transductive-zero-shot-classification&page=1"> Transductive Zero-Shot Classification <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=tumor-segmentation&page=1"> Tumor Segmentation <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=video-anomaly-detection&page=1"> Video Anomaly Detection <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=video-generation&page=1"> Video Generation <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=video-summarization&page=1"> Video Summarization <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=word-sense-disambiguation&page=1"> Word Sense Disambiguation <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=zeroshot-video-question-answer&page=1"> Zero-Shot Video Question Answer <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?task=3d-classification&page=1"> 3D Classification <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=3d-object-tracking&page=1"> 3D Object Tracking <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=clustering-algorithms-evaluation&page=1"> Clustering Algorithms Evaluation <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=community-detection&page=1"> Community Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=density-estimation&page=1"> Density Estimation <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=emotion-recognition-in-conversation&page=1"> Emotion Recognition in Conversation <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=handwritten-text-recognition&page=1"> Handwritten Text Recognition <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=human-detection&page=1"> Human Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=image-quality-assessment&page=1"> Image Quality Assessment <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=keypoint-detection&page=1"> Keypoint Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=medical-image-classification&page=1"> Medical Image Classification <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=multiple-instance-learning&page=1"> Multiple Instance Learning <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=named-entity-recognition-1&page=1"> Named Entity Recognition <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=open-domain-dialog&page=1"> Open-Domain Dialog <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=pose-tracking&page=1"> Pose Tracking <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=saliency-detection&page=1"> Saliency Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=semi-supervised-semantic-segmentation&page=1"> Semi-Supervised Semantic Segmentation <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=semi-supervised-video-object-segmentation&page=1"> Semi-Supervised Video Object Segmentation <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=sentence-classification&page=1"> Sentence Classification <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=single-view-3d-reconstruction&page=1"> Single-View 3D Reconstruction <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=small-object-detection&page=1"> Small Object Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=weakly-supervised-object-detection&page=1"> Weakly Supervised Object Detection <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?task=activity-detection&page=1"> Activity Detection <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=binary-classification&page=1"> Binary Classification <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=code-completion&page=1"> Code Completion <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=dialogue-state-tracking&page=1"> Dialogue State Tracking <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=hierarchical-multi-label-classification&page=1"> Hierarchical Multi-label Classification <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=image-registration&page=1"> Image Registration <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=image-restoration&page=1"> Image Restoration <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=imputation&page=1"> Imputation <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=intent-classification&page=1"> Intent Classification <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=knowledge-base-question-answering&page=1"> Knowledge Base Question Answering <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=physical-simulations&page=1"> Physical Simulations <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=semi-supervised-image-classification&page=1"> Semi-Supervised Image Classification <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=uie&page=1"> UIE <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=video-quality-assessment&page=1"> Video Quality Assessment <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=vision-and-language-navigation&page=1"> Vision and Language Navigation <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=motion-prediction&page=1"> motion prediction <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?task=2d-pose-estimation&page=1"> 2D Pose Estimation <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=3d-face-reconstruction&page=1"> 3D Face Reconstruction <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=adversarial-attack&page=1"> Adversarial Attack <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=adversarial-robustness&page=1"> Adversarial Robustness <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=covid-19-detection&page=1"> COVID-19 Diagnosis <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=chatbot&page=1"> Chatbot <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=downbeat-tracking&page=1"> Downbeat Tracking <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=event-based-vision&page=1"> Event-based vision <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=generalizable-person-re-identification&page=1"> Generalizable Person Re-identification <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=medical-visual-question-answering&page=1"> Medical Visual Question Answering <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=motion-forecasting&page=1"> Motion Forecasting <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=multi-label-text-classification&page=1"> Multi-Label Text Classification <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=multi-class-classification&page=1"> Multi-class Classification <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=network-intrusion-detection&page=1"> Network Intrusion Detection <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=real-time-semantic-segmentation&page=1"> Real-Time Semantic Segmentation <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=referring-expression-comprehension&page=1"> Referring Expression Comprehension <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=relational-reasoning&page=1"> Relational Reasoning <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=sentiment-classification&page=1"> Sentiment Classification <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=speaker-verification&page=1"> Speaker Verification <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=stochastic-optimization&page=1"> Stochastic Optimization <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=surface-normals-estimation&page=1"> Surface Normals Estimation <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?task=acoustic-scene-classification&page=1"> Acoustic Scene Classification <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=answer-selection&page=1"> Answer Selection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=audio-generation&page=1"> Audio Generation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=automatic-post-editing&page=1"> Automatic Post-Editing <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=boundary-detection&page=1"> Boundary Detection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=citation-recommendation&page=1"> Citation Recommendation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=column-type-annotation&page=1"> Column Type Annotation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=continuous-control&page=1"> Continuous Control <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=conversational-response-selection&page=1"> Conversational Response Selection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=depth-completion&page=1"> Depth Completion <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=disentanglement&page=1"> Disentanglement <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=entity-disambiguation&page=1"> Entity Disambiguation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=entity-typing&page=1"> Entity Typing <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=facial-attribute-classification&page=1"> Facial Attribute Classification <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=fact-checking&page=1"> Fact Checking <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=fine-grained-image-recognition&page=1"> Fine-Grained Image Recognition <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=fraud-detection&page=1"> Fraud Detection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=graph-embedding&page=1"> Graph Embedding <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=head-pose-estimation&page=1"> Head Pose Estimation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=human-activity-recognition&page=1"> Human Activity Recognition <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=image-compression&page=1"> Image Compression <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=image-manipulation&page=1"> Image Manipulation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=mathematical-question-answering&page=1"> Mathematical Question Answering <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=music-transcription&page=1"> Music Transcription <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=nested-named-entity-recognition&page=1"> Nested Named Entity Recognition <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=news-classification&page=1"> News Classification <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=object-detection-in-indoor-scenes&page=1"> Object Detection In Indoor Scenes <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=outlier-detection&page=1"> Outlier Detection <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=referring-expression-segmentation&page=1"> Referring Expression Segmentation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=sentence-embeddings&page=1"> Sentence Embeddings <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=table-annotation&page=1"> Table annotation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=time-series-1&page=1"> Time Series <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=time-series-prediction&page=1"> Time Series Prediction <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=token-classification&page=1"> Token Classification <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=unsupervised-object-segmentation&page=1"> Unsupervised Object Segmentation <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=video-object-tracking&page=1"> Video Object Tracking <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=virtual-try-on&page=1"> Virtual Try-on <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?task=task&page=1"> <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=3d-depth-estimation&page=1"> 3D Depth Estimation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=3d-medical-imaging-segmentation&page=1"> 3D Medical Imaging Segmentation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=abusive-language&page=1"> Abusive Language <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=action-anticipation&page=1"> Action Anticipation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=action-quality-assessment&page=1"> Action Quality Assessment <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=audio-tagging&page=1"> Audio Tagging <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=bias-detection&page=1"> Bias Detection <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=color-image-denoising&page=1"> Color Image Denoising <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=conditional-image-generation&page=1"> Conditional Image Generation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=conversational-question-answering&page=1"> Conversational Question Answering <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=cross-lingual-ner&page=1"> Cross-Lingual NER <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=dimensionality-reduction&page=1"> Dimensionality Reduction <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=discourse-parsing&page=1"> Discourse Parsing <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=entity-resolution&page=1"> Entity Resolution <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=federated-learning&page=1"> Federated Learning <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=few-shot-audio-classification&page=1"> Few-Shot Audio Classification <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=graph-learning&page=1"> Graph Learning <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=graph-matching&page=1"> Graph Matching <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=handwriting-generation&page=1"> Handwriting generation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=image-reconstruction&page=1"> Image Reconstruction <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=indoor-localization&page=1"> Indoor Localization <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=key-information-extraction&page=1"> Key Information Extraction <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=license-plate-detection&page=1"> License Plate Detection <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=material-recognition&page=1"> Material Recognition <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=multi-label-image-classification&page=1"> Multi-Label Image Classification <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=multiview-detection&page=1"> Multiview Detection <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=object-detection-in-aerial-images&page=1"> Object Detection In Aerial Images <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=opinion-mining&page=1"> Opinion Mining <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=point-cloud-registration&page=1"> Point Cloud Registration <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=reinforcement-learning&page=1"> Reinforcement Learning <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=scene-generation&page=1"> Scene Generation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=speaker-diarization&page=1"> Speaker Diarization <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=time-series-anomaly-detection&page=1"> Time Series Anomaly Detection <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=topic-classification&page=1"> Topic Classification <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=unsupervised-person-re-identification&page=1"> Unsupervised Person Re-Identification <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=video-inpainting&page=1"> Video Inpainting <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=video-object-detection&page=1"> Video Object Detection <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=video-recognition&page=1"> Video Recognition <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=video-segmentation&page=1"> Video Segmentation <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=visual-dialogue&page=1"> Visual Dialog <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=visual-grounding&page=1"> Visual Grounding <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=within-session-motor-imagery-left-hand-vs&page=1"> Within-Session Motor Imagery (left hand vs. right hand) <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?task=3d-object-recognition&page=1"> 3D Object Recognition <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=3d-shape-reconstruction&page=1"> 3D Shape Reconstruction <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=6d-pose-estimation-using-rgbd&page=1"> 6D Pose Estimation using RGBD <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=ad-hoc-video-search&page=1"> Ad-hoc video search <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=arithmetic-reasoning&page=1"> Arithmetic Reasoning <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=automated-theorem-proving&page=1"> Automated Theorem Proving <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=blind-super-resolution&page=1"> Blind Super-Resolution <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=breast-cancer-detection&page=1"> Breast Cancer Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=camera-localization&page=1"> Camera Localization <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=causal-inference&page=1"> Causal Inference <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=change-point-detection&page=1"> Change Point Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=chart-question-answering&page=1"> Chart Question Answering <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=chinese-reading-comprehension&page=1"> Chinese Reading Comprehension <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=click-through-rate-prediction&page=1"> Click-Through Rate Prediction <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=code-classification&page=1"> Code Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=code-summarization-1&page=1"> Code Summarization <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=code-translation&page=1"> Code Translation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=colorectal-polyps-characterization&page=1"> Colorectal Polyps Characterization <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=defect-detection&page=1"> Defect Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=disaster-response&page=1"> Disaster Response <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=ecg-classification&page=1"> ECG Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=edge-detection&page=1"> Edge Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=explanation-generation&page=1"> Explanation Generation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=gait-recognition&page=1"> Gait Recognition <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=classification&page=1"> General Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=generalized-zero-shot-learning&page=1"> Generalized Zero-Shot Learning <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=hyperspectral-image-classification&page=1"> Hyperspectral Image Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=incremental-learning&page=1"> Incremental Learning <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=lidar-semantic-segmentation&page=1"> LIDAR Semantic Segmentation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=medical-image-registration&page=1"> Medical Image Registration <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=multi-label-learning&page=1"> Multi-Label Learning <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=multi-agent-reinforcement-learning&page=1"> Multi-agent Reinforcement Learning <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=multiple-choice&page=1"> Multiple-choice <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=online-multi-object-tracking&page=1"> Online Multi-Object Tracking <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=person-identification&page=1"> Person Identification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=person-search&page=1"> Person Search <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=remote-sensing-image-classification&page=1"> Remote Sensing Image Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=robust-classification&page=1"> Robust classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=satellite-image-classification&page=1"> Satellite Image Classification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=scene-segmentation&page=1"> Scene Segmentation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=shadow-removal&page=1"> Shadow Removal <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=sound-event-localization-and-detection&page=1"> Sound Event Localization and Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=temporal-tagging&page=1"> Temporal Tagging <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=text-to-video-generation&page=1"> Text-to-Video Generation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=unsupervised-object-detection&page=1"> Unsupervised Object Detection <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=unsupervised-video-object-segmentation&page=1"> Unsupervised Video Object Segmentation <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=vehicle-re-identification&page=1"> Vehicle Re-Identification <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=video-reconstruction&page=1"> Video Reconstruction <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?task=3d-human-shape-estimation&page=1"> 3D Human Shape Estimation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=3d-multi-object-tracking&page=1"> 3D Multi-Object Tracking <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=3d-object-reconstruction&page=1"> 3D Object Reconstruction <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=anomaly-detection-in-surveillance-videos&page=1"> Anomaly Detection In Surveillance Videos <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=argument-mining&page=1"> Argument Mining <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=audio-visual-speech-recognition&page=1"> Audio-Visual Speech Recognition <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=automl&page=1"> AutoML <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=bayesian-inference&page=1"> Bayesian Inference <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=camouflaged-object-segmentation&page=1"> Camouflaged Object Segmentation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=colorization&page=1"> Colorization <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=data-integration&page=1"> Data Integration <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=dense-video-captioning&page=1"> Dense Video Captioning <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=dialogue-understanding&page=1"> Dialogue Understanding <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=document-layout-analysis&page=1"> Document Layout Analysis <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=dynamic-link-prediction&page=1"> Dynamic Link Prediction <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=eeg&page=1"> Electroencephalogram (EEG) <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=event-detection&page=1"> Event Detection <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=face-identification&page=1"> Face Identification <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=face-swapping&page=1"> Face Swapping <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=font-recognition&page=1"> Font Recognition <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=heterogeneous-node-classification&page=1"> Heterogeneous Node Classification <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=homography-estimation&page=1"> Homography Estimation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=human-interaction-recognition&page=1"> Human Interaction Recognition <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=human-part-segmentation&page=1"> Human Part Segmentation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=image-manipulation-localization&page=1"> Image Manipulation Localization <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=interactive-segmentation&page=1"> Interactive Segmentation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=intrinsic-image-decomposition&page=1"> Intrinsic Image Decomposition <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=kg-to-text&page=1"> KG-to-Text Generation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=keyword-spotting&page=1"> Keyword Spotting <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=monocular-visual-odometry&page=1"> Monocular Visual Odometry <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=motion-estimation&page=1"> Motion Estimation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=multimodal-emotion-recognition&page=1"> Multimodal Emotion Recognition <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=music-classification&page=1"> Music Classification <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=offline-rl&page=1"> Offline RL <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=ood-detection&page=1"> Out of Distribution (OOD) Detection <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=passage-retrieval&page=1"> Passage Retrieval <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=product-recommendation&page=1"> Product Recommendation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=program-repair&page=1"> Program Repair <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=real-time-object-detection&page=1"> Real-Time Object Detection <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=robust-object-detection&page=1"> Robust Object Detection <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=saliency-prediction&page=1"> Saliency Prediction <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=sequential-recommendation&page=1"> Sequential Recommendation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=skin-lesion-classification&page=1"> Skin Lesion Classification <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=code-summarization&page=1"> Source Code Summarization <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=stance-classification&page=1"> Stance Classification <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=table-detection&page=1"> Table Detection <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=text-segmentation&page=1"> Text Segmentation <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=time-series-regression&page=1"> Time Series Regression <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?task=traffic-sign-recognition&page=1"> Traffic Sign Recognition <span class="badge badge-light">7</span> </a> </div> </div> <div class="datasets-filter p-md-2 p-lg-3"> <div class="filter-name"> Filter by Language </div> <div class="filter-items"> <a class="filter-item" href="?lang=english&page=1"> English <span class="badge badge-light">3390</span> </a> <a class="filter-item" href="?lang=chinese&page=1"> Chinese <span class="badge badge-light">388</span> </a> <a class="filter-item" href="?lang=german&page=1"> German <span class="badge badge-light">183</span> </a> <a class="filter-item" href="?lang=french&page=1"> French <span class="badge badge-light">173</span> </a> <a class="filter-item" href="?lang=spanish&page=1"> Spanish <span class="badge badge-light">141</span> </a> <a class="filter-item" href="?lang=russian&page=1"> Russian <span class="badge badge-light">125</span> </a> <a class="filter-item" href="?lang=japanese&page=1"> Japanese <span class="badge badge-light">98</span> </a> <a class="filter-item" href="?lang=arabic&page=1"> Arabic <span class="badge badge-light">91</span> </a> <a class="filter-item" href="?lang=italian&page=1"> Italian <span class="badge badge-light">90</span> </a> <a class="filter-item" href="?lang=portuguese&page=1"> Portuguese <span class="badge badge-light">84</span> </a> <a class="filter-item" href="?lang=hindi&page=1"> Hindi <span class="badge badge-light">75</span> </a> <a class="filter-item" href="?lang=vietnamese&page=1"> Vietnamese <span class="badge badge-light">66</span> </a> <a class="filter-item" href="?lang=korean&page=1"> Korean <span class="badge badge-light">62</span> </a> <a class="filter-item" href="?lang=turkish&page=1"> Turkish <span class="badge badge-light">56</span> </a> <a class="filter-item" href="?lang=bengali&page=1"> Bengali <span class="badge badge-light">55</span> </a> <a class="filter-item" href="?lang=persian&page=1"> Persian <span class="badge badge-light">51</span> </a> <a class="filter-item" href="?lang=dutch&page=1"> Dutch <span class="badge badge-light">45</span> </a> <a class="filter-item" href="?lang=tamil&page=1"> Tamil <span class="badge badge-light">44</span> </a> <a class="filter-item" href="?lang=polish&page=1"> Polish <span class="badge badge-light">40</span> </a> <a class="filter-item" href="?lang=czech&page=1"> Czech <span class="badge badge-light">39</span> </a> <a class="filter-item" href="?lang=indonesian&page=1"> Indonesian <span class="badge badge-light">38</span> </a> <a class="filter-item" href="?lang=danish&page=1"> Danish <span class="badge badge-light">34</span> </a> <a class="filter-item" href="?lang=finnish&page=1"> Finnish <span class="badge badge-light">33</span> </a> <a class="filter-item" href="?lang=romanian&page=1"> Romanian <span class="badge badge-light">33</span> </a> <a class="filter-item" href="?lang=telugu&page=1"> Telugu <span class="badge badge-light">31</span> </a> <a class="filter-item" href="?lang=thai&page=1"> Thai <span class="badge badge-light">29</span> </a> <a class="filter-item" href="?lang=multilingual&page=1"> Multilingual <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?lang=swedish&page=1"> Swedish <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?lang=urdu&page=1"> Urdu <span class="badge badge-light">28</span> </a> <a class="filter-item" href="?lang=marathi&page=1"> Marathi <span class="badge badge-light">27</span> </a> <a class="filter-item" href="?lang=hungarian&page=1"> Hungarian <span class="badge badge-light">26</span> </a> <a class="filter-item" href="?lang=greek&page=1"> Greek <span class="badge badge-light">24</span> </a> <a class="filter-item" href="?lang=estonian&page=1"> Estonian <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?lang=gujarati&page=1"> Gujarati <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?lang=mandarin-chinese&page=1"> Mandarin Chinese <span class="badge badge-light">23</span> </a> <a class="filter-item" href="?lang=hebrew&page=1"> Hebrew <span class="badge badge-light">22</span> </a> <a class="filter-item" href="?lang=bulgarian&page=1"> Bulgarian <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?lang=malayalam&page=1"> Malayalam <span class="badge badge-light">21</span> </a> <a class="filter-item" href="?lang=basque&page=1"> Basque <span class="badge badge-light">19</span> </a> <a class="filter-item" href="?lang=kannada&page=1"> Kannada <span class="badge badge-light">18</span> </a> <a class="filter-item" href="?lang=catalan&page=1"> Catalan <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?lang=panjabi&page=1"> Punjabi <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?lang=slovak&page=1"> Slovak <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?lang=swahili&page=1"> Swahili <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?lang=ukrainian&page=1"> Ukrainian <span class="badge badge-light">17</span> </a> <a class="filter-item" href="?lang=latvian&page=1"> Latvian <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?lang=slovenian&page=1"> Slovenian <span class="badge badge-light">16</span> </a> <a class="filter-item" href="?lang=croatian&page=1"> Croatian <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?lang=lithuanian&page=1"> Lithuanian <span class="badge badge-light">15</span> </a> <a class="filter-item" href="?lang=kazakh&page=1"> Kazakh <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?lang=norwegian&page=1"> Norwegian <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?lang=serbian&page=1"> Serbian <span class="badge badge-light">14</span> </a> <a class="filter-item" href="?lang=amharic&page=1"> Amharic <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?lang=iranian-persian&page=1"> Iranian Persian <span class="badge badge-light">13</span> </a> <a class="filter-item" href="?lang=kurdish&page=1"> Kurdish <span class="badge badge-light">12</span> </a> <a class="filter-item" href="?lang=albanian&page=1"> Albanian <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?lang=assamese&page=1"> Assamese <span class="badge badge-light">11</span> </a> <a class="filter-item" href="?lang=irish&page=1"> Irish <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?lang=welsh&page=1"> Welsh <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?lang=yoruba&page=1"> Yoruba <span class="badge badge-light">10</span> </a> <a class="filter-item" href="?lang=american-sign-language&page=1"> American Sign Language <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=armenian&page=1"> Armenian <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=macedonian&page=1"> Macedonian <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=maltese&page=1"> Maltese <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=mongolian&page=1"> Mongolian <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=sanskrit&page=1"> Sanskrit <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=tagalog&page=1"> Tagalog <span class="badge badge-light">9</span> </a> <a class="filter-item" href="?lang=azerbaijani&page=1"> Azerbaijani <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=breton&page=1"> Breton <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=burmese&page=1"> Burmese <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=hausa&page=1"> Hausa <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=igbo&page=1"> Igbo <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=odia&page=1"> Odia <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=oriya-macrolanguage&page=1"> Oriya (macrolanguage) <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=sinhala&page=1"> Sinhala <span class="badge badge-light">8</span> </a> <a class="filter-item" href="?lang=esperanto&page=1"> Esperanto <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?lang=filipino&page=1"> Filipino <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?lang=galician&page=1"> Galician <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?lang=georgian&page=1"> Georgian <span class="badge badge-light">7</span> </a> <a class="filter-item" href="?lang=bambara&page=1"> Bambara <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=guarani&page=1"> Guarani <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=icelandic&page=1"> Icelandic <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=malagasy&page=1"> Malagasy <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=nepali-macrolanguage&page=1"> Nepali (macrolanguage) <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=nigerian-pidgin&page=1"> Nigerian Pidgin <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=oromo&page=1"> Oromo <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=serbo-croatian&page=1"> Serbo-Croatian <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=somali&page=1"> Somali <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=uzbek&page=1"> Uzbek <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=western-panjabi&page=1"> Western Panjabi <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=wolof&page=1"> Wolof <span class="badge badge-light">6</span> </a> <a class="filter-item" href="?lang=afrikaans&page=1"> Afrikaans <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=belarusian&page=1"> Belarusian <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=bosnian&page=1"> Bosnian <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=central-khmer&page=1"> Central Khmer <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=central-kurdish&page=1"> Central Kurdish <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=fon&page=1"> Fon <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=ganda&page=1"> Ganda <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=haitian&page=1"> Haitian <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=javanese&page=1"> Javanese <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=latin&page=1"> Latin <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=norwegian-nynorsk&page=1"> Norwegian Nynorsk <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=quechua&page=1"> Quechua <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=scottish-gaelic&page=1"> Scottish Gaelic <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=sindhi&page=1"> Sindhi <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=sundanese&page=1"> Sundanese <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=tibetan&page=1"> Tibetan <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=tigrinya&page=1"> Tigrinya <span class="badge badge-light">5</span> </a> <a class="filter-item" href="?lang=aymara&page=1"> Aymara <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=bangala&page=1"> Bangala <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=chechen&page=1"> Chechen <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=dhivehi&page=1"> Dhivehi <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=egyptian-arabic&page=1"> Egyptian Arabic <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=ewe&page=1"> Ewe <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=kabyle&page=1"> Kabyle <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=lingala&page=1"> Lingala <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=malay-individual-language&page=1"> Malay (individual language) <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=norwegian-bokmal&page=1"> Norwegian Bokmål <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=standard-arabic&page=1"> Standard Arabic <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=tatar&page=1"> Tatar <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=tetum&page=1"> Tetum <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=tswana&page=1"> Tswana <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=twi&page=1"> Twi <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=upper-sorbian&page=1"> Upper Sorbian <span class="badge badge-light">4</span> </a> <a class="filter-item" href="?lang=aragonese&page=1"> Aragonese <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=bashkir&page=1"> Bashkir <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=bavarian&page=1"> Bavarian <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=bishnupriya&page=1"> Bishnupriya <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=cebuano&page=1"> Cebuano <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=chuvash&page=1"> Chuvash <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=erzya&page=1"> Erzya <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=faroese&page=1"> Faroese <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=fulah&page=1"> Fulah <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=goan-konkani&page=1"> Goan Konkani <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=iloko&page=1"> Iloko <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=interlingue&page=1"> Interlingue <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=kinyarwanda&page=1"> Kinyarwanda <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=kirghiz&page=1"> Kirghiz <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=lao&page=1"> Lao <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=luo-kenya-and-tanzania&page=1"> Luo (Kenya and Tanzania) <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=maithili&page=1"> Maithili <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=nyanja&page=1"> Nyanja <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=occitan-post-1500&page=1"> Occitan (post 1500) <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=romansh&page=1"> Romansh <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=rundi&page=1"> Rundi <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=russia-buriat&page=1"> Russia Buriat <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=sardinian&page=1"> Sardinian <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=south-azerbaijani&page=1"> South Azerbaijani <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=swiss-german&page=1"> Swiss German <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=turkmen&page=1"> Turkmen <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=uighur&page=1"> Uighur <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=xhosa&page=1"> Xhosa <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=yiddish&page=1"> Yiddish <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=zulu&page=1"> Zulu <span class="badge badge-light">3</span> </a> <a class="filter-item" href="?lang=argentine-sign-language&page=1"> Argentine Sign Language <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=asturian&page=1"> Asturian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=avaric&page=1"> Avaric <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=bangladeshi-sign-language&page=1"> Bangladeshi Sign Language <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=bhojpuri&page=1"> Bhojpuri <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=central-bikol&page=1"> Central Bikol <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=cherokee&page=1"> Cherokee <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=church-slavic&page=1"> Church Slavic <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=cornish&page=1"> Cornish <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=corsican&page=1"> Corsican <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=dimli-individual-language&page=1"> Dimli (individual language) <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=eastern-mari&page=1"> Eastern Mari <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=german-sign-language&page=1"> German Sign Language <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=gothic&page=1"> Gothic <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=ido&page=1"> Ido <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=inuktitut&page=1"> Inuktitut <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=jamaican-creole-english&page=1"> Jamaican Creole English <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=jejueo&page=1"> Jejueo <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=kalaallisut&page=1"> Kalaallisut <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=kalmyk&page=1"> Kalmyk <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=karachay-balkar&page=1"> Karachay-Balkar <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=komi&page=1"> Komi <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=komi-permyak&page=1"> Komi-Permyak <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=lezghian&page=1"> Lezghian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=limburgan&page=1"> Limburgan <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=livvi&page=1"> Livvi <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=lojban&page=1"> Lojban <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=lombard&page=1"> Lombard <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=low-german&page=1"> Low German <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=lower-sorbian&page=1"> Lower Sorbian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=luxembourgish&page=1"> Luxembourgish <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=malay-macrolanguage&page=1"> Malay (macrolanguage) <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=manipuri&page=1"> Manipuri <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=manx&page=1"> Manx <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=mazanderani&page=1"> Mazanderani <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=minangkabau&page=1"> Minangkabau <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=mingrelian&page=1"> Mingrelian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=mirandese&page=1"> Mirandese <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=modern-greek&page=1"> Modern Greek <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=moksha&page=1"> Moksha <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=mossi&page=1"> Mossi <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=naxi&page=1"> Naxi <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=neapolitan&page=1"> Neapolitan <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=newari&page=1"> Newari <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=northern-frisian&page=1"> Northern Frisian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=northern-kurdish&page=1"> Northern Kurdish <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=northern-luri&page=1"> Northern Luri <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=northern-sami&page=1"> Northern Sami <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=old-spanish&page=1"> Old Spanish <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=ossetian&page=1"> Ossetian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=pampanga&page=1"> Pampanga <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=piemontese&page=1"> Piemontese <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=pushto&page=1"> Pushto <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=shona&page=1"> Shona <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=sichuan-yi&page=1"> Sichuan Yi <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=sicilian&page=1"> Sicilian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=swati&page=1"> Swati <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=swiss-german-sign-language&page=1"> Swiss-German Sign Language <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=tai&page=1"> Tai <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=tajik&page=1"> Tajik <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=tsonga&page=1"> Tsonga <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=turkish-sign-language&page=1"> Turkish Sign Language <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=tuvinian&page=1"> Tuvinian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=udmurt&page=1"> Udmurt <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=venetian&page=1"> Venetian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=volapuk&page=1"> Volapük <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=walloon&page=1"> Walloon <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=waray-philippines&page=1"> Waray (Philippines) <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=western-frisian&page=1"> Western Frisian <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=western-mari&page=1"> Western Mari <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=wu-chinese&page=1"> Wu Chinese <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=yakut&page=1"> Yakut <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=yue-chinese&page=1"> Yue Chinese <span class="badge badge-light">2</span> </a> <a class="filter-item" href="?lang=abkhazian&page=1"> Abkhazian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=achinese&page=1"> Achinese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=adyghe&page=1"> Adyghe <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=afar&page=1"> Afar <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=akan&page=1"> Akan <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=akkadian&page=1"> Akkadian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=akuntsu&page=1"> Akuntsu <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ambonese-malay&page=1"> Ambonese Malay <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ancient-greek&page=1"> Ancient Greek <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ancient-hebrew&page=1"> Ancient Hebrew <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=apurina&page=1"> Apurinã <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=arpitan&page=1"> Arpitan <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=assyrian-neo-aramaic&page=1"> Assyrian Neo-Aramaic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=banjar&page=1"> Banjar <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=bemba-zambia&page=1"> Bemba (Zambia) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=bislama&page=1"> Bislama <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=bodo-india&page=1"> Bodo (India) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=buginese&page=1"> Buginese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=central-pashto&page=1"> Central Pashto <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=chamorro&page=1"> Chamorro <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=chavacano&page=1"> Chavacano <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=cheyenne&page=1"> Cheyenne <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=choctaw&page=1"> Choctaw <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=chukot&page=1"> Chukot <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=congo-swahili&page=1"> Congo Swahili <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=coptic&page=1"> Coptic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=cree&page=1"> Cree <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=creek&page=1"> Creek <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=crimean-tatar&page=1"> Crimean Tatar <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=dogri-macrolanguage&page=1"> Dogri (macrolanguage) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=dzongkha&page=1"> Dzongkha <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=extremaduran&page=1"> Extremaduran <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=fiji-hindi&page=1"> Fiji Hindi <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=fijian&page=1"> Fijian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=french-sign-language&page=1"> French Sign Language <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=friulian&page=1"> Friulian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=gagauz&page=1"> Gagauz <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=gan-chinese&page=1"> Gan Chinese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=geez&page=1"> Geez <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=gilaki&page=1"> Gilaki <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=greek-sign-language&page=1"> Greek Sign Language <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=gulf-arabic&page=1"> Gulf Arabic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=hakha-chin&page=1"> Hakha Chin <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=hakka-chinese&page=1"> Hakka Chinese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=halh-mongolian&page=1"> Halh Mongolian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=hawaiian&page=1"> Hawaiian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=herero&page=1"> Herero <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=hiri-motu&page=1"> Hiri Motu <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=interlingua-international-auxiliary-language&page=1"> Interlingua (International Auxiliary Language Association) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=inupiaq&page=1"> Inupiaq <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kabardian&page=1"> Kabardian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kanuri&page=1"> Kanuri <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kara-kalpak&page=1"> Kara-Kalpak <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=karelian&page=1"> Karelian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kashmiri&page=1"> Kashmiri <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kashubian&page=1"> Kashubian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=khunsari&page=1"> Khunsari <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kikuyu&page=1"> Kikuyu <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=komi-zyrian&page=1"> Komi-Zyrian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kongo&page=1"> Kongo <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=krio&page=1"> Krio <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kuanyama&page=1"> Kuanyama <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kupang-malay&page=1"> Kupang Malay <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=kolsch&page=1"> Kölsch <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ladino&page=1"> Ladino <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=lak&page=1"> Lak <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=latgalian&page=1"> Latgalian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ligurian&page=1"> Ligurian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=literary-chinese&page=1"> Literary Chinese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=lozi&page=1"> Lozi <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=lunda&page=1"> Lunda <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=luo-cameroon&page=1"> Luo (Cameroon) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=lushai&page=1"> Lushai <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=makasar&page=1"> Makasar <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=malayic-dayak&page=1"> Malayic Dayak <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=maori&page=1"> Maori <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=marshallese&page=1"> Marshallese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=mbya-guarani&page=1"> Mbyá Guaraní <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=min-dong-chinese&page=1"> Min Dong Chinese <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=modern-greek-1453&page=1"> Modern Greek (1453-) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=moroccan-arabic&page=1"> Moroccan Arabic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=munduruku&page=1"> Mundurukú <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=narom&page=1"> Narom <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=nauru&page=1"> Nauru <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=navajo&page=1"> Navajo <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=nayini&page=1"> Nayini <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=ndonga&page=1"> Ndonga <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=nepali-individual-language&page=1"> Nepali (individual language) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=novial&page=1"> Novial <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=official-aramaic-700-300-bce&page=1"> Official Aramaic (700-300 BCE) <span class="badge 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Pennsylvania German <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=pfaelzisch&page=1"> Pfaelzisch <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=picard&page=1"> Picard <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=pitcairn-norfolk&page=1"> Pitcairn-Norfolk <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=pontic&page=1"> Pontic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=rajasthani&page=1"> Rajasthani <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=rusyn&page=1"> Rusyn <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=samoan&page=1"> Samoan <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=sango&page=1"> Sango <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=saterfriesisch&page=1"> Saterfriesisch <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=scots&page=1"> Scots <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=silesian&page=1"> Silesian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=skolt-sami&page=1"> Skolt Sami <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=soi&page=1"> Soi <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=south-levantine-arabic&page=1"> South Levantine Arabic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=southern-sotho&page=1"> Southern Sotho <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=sranan-tongo&page=1"> Sranan Tongo <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=swahili-macrolanguage&page=1"> Swahili (macrolanguage) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=swedish-sign-language&page=1"> Swedish Sign Language <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tahitian&page=1"> Tahitian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tok-pisin&page=1"> Tok Pisin <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tonga-tonga-islands&page=1"> Tonga (Tonga Islands) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tonga-zambia&page=1"> Tonga (Zambia) <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tosk-albanian&page=1"> Tosk Albanian <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tulu&page=1"> Tulu <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tumbuka&page=1"> Tumbuka <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tunisian-arabic&page=1"> Tunisian Arabic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=tupinamba&page=1"> Tupinambá <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=uab-meto&page=1"> Uab Meto <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=venda&page=1"> Venda <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=veps&page=1"> Veps <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=vlaams&page=1"> Vlaams <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=vlax-romani&page=1"> Vlax Romani <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=votic&page=1"> Votic <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=warlpiri&page=1"> Warlpiri <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=zaza&page=1"> Zaza <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=zeeuws&page=1"> Zeeuws <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=zhuang&page=1"> Zhuang <span class="badge badge-light">1</span> </a> <a class="filter-item" href="?lang=dogri-individual-language&page=1"> Dogri (individual language) <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=kabuverdianu&page=1"> Kabuverdianu <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=kachin&page=1"> Kachin <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=lingua-franca&page=1"> Lingua Franca <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=mesopotamian-arabic&page=1"> Mesopotamian Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=najdi-arabic&page=1"> Najdi Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=nigerian-fulfulde&page=1"> Nigerian Fulfulde <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=north-azerbaijani&page=1"> North Azerbaijani <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=north-levantine-arabic&page=1"> North Levantine Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=northern-huishui-hmong&page=1"> Northern Huishui Hmong <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=northern-uzbek&page=1"> Northern Uzbek <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=plateau-malagasy&page=1"> Plateau Malagasy <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=portuguse&page=1"> Portuguse <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=saidi-arabic&page=1"> Saidi Arabic <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=santali&page=1"> Santali <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=shan&page=1"> Shan <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=southern-pashto&page=1"> Southern Pashto <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=standard-latvian&page=1"> Standard Latvian <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=thai-song&page=1"> Thai Song <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=tunisian-sign-language&page=1"> Tunisian Sign Language <span class="badge badge-light">0</span> </a> <a class="filter-item" href="?lang=west-central-oromo&page=1"> West Central Oromo <span class="badge badge-light">0</span> </a> </div> </div> </div> </div> </div> <div class=" col-md-9"> <h1 class="results-count">10971 dataset results </h1> <div class="datasets-items show-list"> <div class="dataset-wide-box"> <a href="/dataset/cifar-10"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/52296e10-6143-483a-8eff-41b6f2a724e6.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CIFAR-10 <span class="full-name">(Canadian Institute for Advanced Research, 10 classes)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/9101c960-563f-4bfa-bde0-ee5a5977d43b.jpg"> <p> The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class. </p> <p class="description-stats"> 15,203 <span class="smaller-text">PAPERS</span> • 107 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/imagenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000008-96e9e5fa_mu7w2J9.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ImageNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000008-a9fe912a.jpg"> <p> 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. </p> <p class="description-stats"> 14,469 <span class="smaller-text">PAPERS</span> • 51 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/coco"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/4c18f69d-02e3-4ce8-8027-d8863a034baf.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MS COCO <span class="full-name">(Microsoft Common Objects in Context)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/9f876b0a-7751-43bc-9f80-cbd8c2adabf3.jpg"> <p> The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images. </p> <p class="description-stats"> 11,128 <span class="smaller-text">PAPERS</span> • 96 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/cifar-100"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000433-f10eb625_opP7BEW.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CIFAR-100 </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000433-02db1517.jpg"> <p> The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. </p> <p class="description-stats"> 8,387 <span class="smaller-text">PAPERS</span> • 58 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mnist"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000001-f66c5dc9_UOPLOsj.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MNIST </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000001-5ee62d70.jpg"> <p> The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. </p> <p class="description-stats"> 7,330 <span class="smaller-text">PAPERS</span> • 52 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/cityscapes"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003508-c42966f0_4LFY4pG.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Cityscapes </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003508-7a5c4248.jpg"> <p> Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background. </p> <p class="description-stats"> 3,521 <span class="smaller-text">PAPERS</span> • 54 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/kitti"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000061-51345f15_KglZMVu.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> KITTI </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000061-70c4656d.jpg"> <p> KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odome </p> <p class="description-stats"> 3,449 <span class="smaller-text">PAPERS</span> • 142 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/nerf"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #A59F78;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">NeRF</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> NeRF <span class="full-name">(Neural Radiance Fields)</span> </span> <div class="description"> <p> Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels. </p> <p class="description-stats"> 3,315 <span class="smaller-text">PAPERS</span> • 1 <span class="smaller-text">BENCHMARK</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/celeba"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000002-c0f35985_jbUNXZ4.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CelebA <span class="full-name">(CelebFaces Attributes Dataset)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000002-e6adb1d9.jpg"> <p> CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age. </p> <p class="description-stats"> 3,298 <span class="smaller-text">PAPERS</span> • 23 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/svhn"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000424-f144bd9a_AyzfFXa.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SVHN <span class="full-name">(Street View House Numbers)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000424-e3fabab5.jpg"> <p> Street View House Numbers (SVHN) is a digit classification benchmark dataset that contains 600,000 32×32 RGB images of printed digits (from 0 to 9) cropped from pictures of house number plates. The cropped images are centered in the digit of interest, but nearby digits and other distractors are kept in the image. SVHN has three sets: training, testing sets and an extra set with 530,000 images that are less difficult and can be used for helping with the training process. </p> <p class="description-stats"> 3,242 <span class="smaller-text">PAPERS</span> • 12 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/fashion-mnist"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000040-2c084112_if80Agr.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Fashion-MNIST </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000040-786bb14d.jpg"> <p> Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST. </p> <p class="description-stats"> 2,996 <span class="smaller-text">PAPERS</span> • 18 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/glue"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000005-647c03d7_WZ93UVW.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> GLUE <span class="full-name">(General Language Understanding Evaluation benchmark)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000005-40530a41.jpg"> <p> General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI. </p> <p class="description-stats"> 2,985 <span class="smaller-text">PAPERS</span> • 25 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/sst"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000324-93999024_z0PAkJL.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SST <span class="full-name">(Stanford Sentiment Treebank)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000324-55a42eae.jpg"> <p> The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. </p> <p class="description-stats"> 2,201 <span class="smaller-text">PAPERS</span> • 10 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/librispeech"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003511-19d0318d_vXtAFYe.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> LibriSpeech </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003511-8fb64a65.jpg"> <p> The LibriSpeech corpus is a collection of approximately 1,000 hours of audiobooks that are a part of the LibriVox project. Most of the audiobooks come from the Project Gutenberg. The training data is split into 3 partitions of 100hr, 360hr, and 500hr sets while the dev and test data are split into the ’clean’ and ’other’ categories, respectively, depending upon how well or challenging Automatic Speech Recognition systems would perform against. Each of the dev and test sets is around 5hr in audio length. This corpus also provides the n-gram language models and the corresponding texts excerpted from the Project Gutenberg books, which contain 803M tokens and 977K unique words. </p> <p class="description-stats"> 2,141 <span class="smaller-text">PAPERS</span> • 9 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/cub-200-2011"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000109-b2f499fb_zITIMSh.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CUB-200-2011 <span class="full-name">(Caltech-UCSD Birds-200-2011)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000109-8fb45530.jpg"> <p> The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. </p> <p class="description-stats"> 2,137 <span class="smaller-text">PAPERS</span> • 49 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/squad"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000201-647ae33a_ssxx38Y.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SQuAD <span class="full-name">(Stanford Question Answering Dataset)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000201-3d9e8987.jpg"> <p> The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones. </p> <p class="description-stats"> 2,049 <span class="smaller-text">PAPERS</span> • 13 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/nuscenes"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001251-9084f827_6rFkEsS.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> nuScenes </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001251-e9282896.jpg"> <p> The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. </p> <p class="description-stats"> 1,843 <span class="smaller-text">PAPERS</span> • 21 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/shapenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/ecedbd34-ed50-41be-9dc0-93f495020d99.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ShapeNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/1c3aabfd-32bb-442a-9338-171e145c97bc.jpg"> <p> ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes). </p> <p class="description-stats"> 1,836 <span class="smaller-text">PAPERS</span> • 13 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/multinli"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000006-315f0b20_F9QASlF.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MultiNLI <span class="full-name">(Multi-Genre Natural Language Inference)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000006-cfa91ded.jpg"> <p> The Multi-Genre Natural Language Inference (MultiNLI) dataset has 433K sentence pairs. Its size and mode of collection are modeled closely like SNLI. MultiNLI offers ten distinct genres (Face-to-face, Telephone, 9/11, Travel, Letters, Oxford University Press, Slate, Verbatim, Goverment and Fiction) of written and spoken English data. There are matched dev/test sets which are derived from the same sources as those in the training set, and mismatched sets which do not closely resemble any seen at training time. </p> <p class="description-stats"> 1,753 <span class="smaller-text">PAPERS</span> • 3 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/ucf101"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000066-91ee6b2c_rLkoGNp.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> UCF101 <span class="full-name">(UCF101 Human Actions dataset)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000066-5f399b45.jpg"> <p> UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240. </p> <p class="description-stats"> 1,746 <span class="smaller-text">PAPERS</span> • 25 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/imdb-movie-reviews"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003538-b946fadb_b1MkwaA.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> IMDb Movie Reviews </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003538-2a2e9f11.jpg"> <p> The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data. </p> <p class="description-stats"> 1,699 <span class="smaller-text">PAPERS</span> • 11 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/visual-question-answering"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000005565-0efad15d_dF8vZ6h.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Visual Question Answering <span class="full-name">(VQA)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000005565-4e91b993.jpg"> <p> Visual Question Answering (VQA) is a dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. The first version of the dataset was released in October 2015. VQA v2.0 was released in April 2017. </p> <p class="description-stats"> 1,684 <span class="smaller-text">PAPERS</span> • <span class="smaller-text">NO BENCHMARKS YET</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/sst-2"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #A59F78;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">SST-2</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SST-2 </span> <div class="description"> <p> The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. </p> <p class="description-stats"> 1,666 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mujoco"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001240-38271a8a_4CuxvIx.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MuJoCo </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001240-cd5da8cd.jpg"> <p> MuJoCo (multi-joint dynamics with contact) is a physics engine used to implement environments to benchmark Reinforcement Learning methods. </p> <p class="description-stats"> 1,501 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/scannet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000495-c17eb1ff_o010L4C.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ScanNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000495-af04f025.jpg"> <p> ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects. </p> <p class="description-stats"> 1,420 <span class="smaller-text">PAPERS</span> • 20 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/ffhq"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000590-a43be598_1K0Rf4R.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> FFHQ <span class="full-name">(Flickr-Faces-HQ)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000590-abb4b0b3.jpg"> <p> Flickr-Faces-HQ (FFHQ) consists of 70,000 high-quality PNG images at 1024×1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from Flickr, thus inheriting all the biases of that website, and automatically aligned and cropped using dlib. Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos. </p> <p class="description-stats"> 1,360 <span class="smaller-text">PAPERS</span> • 16 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/modelnet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003524-7d81531f_570qU1z.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ModelNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003524-02957e74.jpg"> <p> The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere. </p> <p class="description-stats"> 1,328 <span class="smaller-text">PAPERS</span> • 18 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mini-imagenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #A59F78;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">mini-Imagenet</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> mini-Imagenet </span> <div class="description"> <p> mini-Imagenet is proposed by Matching Networks for One Shot Learning . In NeurIPS, 2016. This dataset consists of 50000 training images and 10000 testing images, evenly distributed across 100 classes. </p> <p class="description-stats"> 1,302 <span class="smaller-text">PAPERS</span> • 19 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mmlu"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/1beef4bd-2334-4552-847f-2845627473e2.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MMLU <span class="full-name">(Massive Multitask Language Understanding)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/2116b3a8-8377-4d7e-9574-3dc3708ddbf2.jpg"> <p> MMLU (Massive Multitask Language Understanding) is a new benchmark designed to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings. This makes the benchmark more challenging and more similar to how we evaluate humans. The benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. It ranges in difficulty from an elementary level to an advanced professional level, and it tests both world knowledge and problem solving ability. Subjects range from traditional areas, such as mathematics and history, to more specialized areas like law and ethics. The granularity and breadth of the subjects makes the benchmark ideal for identifying a model’s blind spots. </p> <p class="description-stats"> 1,296 <span class="smaller-text">PAPERS</span> • 27 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/kinetics"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000346-272f35cf_HBiP1jT.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Kinetics <span class="full-name">(Kinetics Human Action Video Dataset)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000346-eeec3643.jpg"> <p> The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. </p> <p class="description-stats"> 1,287 <span class="smaller-text">PAPERS</span> • 30 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/snli"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000233-f7c26c46_K6il0jc.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> SNLI <span class="full-name">(Stanford Natural Language Inference)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000233-4dbd08d5.jpg"> <p> The SNLI dataset (Stanford Natural Language Inference) consists of 570k sentence-pairs manually labeled as entailment, contradiction, and neutral. Premises are image captions from Flickr30k, while hypotheses were generated by crowd-sourced annotators who were shown a premise and asked to generate entailing, contradicting, and neutral sentences. Annotators were instructed to judge the relation between sentences given that they describe the same event. Each pair is labeled as “entailment”, “neutral”, “contradiction” or “-”, where “-” indicates that an agreement could not be reached. </p> <p class="description-stats"> 1,271 <span class="smaller-text">PAPERS</span> • 2 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/openai-gym"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000029-cf04ea9d.gif');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> OpenAI Gym </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000029-48aa33a5.gif"> <p> OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It includes environment such as Algorithmic, Atari, Box2D, Classic Control, MuJoCo, Robotics, and Toy Text. </p> <p class="description-stats"> 1,264 <span class="smaller-text">PAPERS</span> • 3 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/visual-genome"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000087-eaaaf55c_cq1PqV1.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Visual Genome </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000087-db925b91.jpg"> <p> Visual Genome contains Visual Question Answering data in a multi-choice setting. It consists of 101,174 images from MSCOCO with 1.7 million QA pairs, 17 questions per image on average. Compared to the Visual Question Answering dataset, Visual Genome represents a more balanced distribution over 6 question types: What, Where, When, Who, Why and How. The Visual Genome dataset also presents 108K images with densely annotated objects, attributes and relationships. </p> <p class="description-stats"> 1,210 <span class="smaller-text">PAPERS</span> • 19 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/natural-questions"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000796-c90d1172_6bTlTQK.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Natural Questions </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000796-a14fb79f.jpg"> <p> The Natural Questions corpus is a question answering dataset containing 307,373 training examples, 7,830 development examples, and 7,842 test examples. Each example is comprised of a google.com query and a corresponding Wikipedia page. Each Wikipedia page has a passage (or long answer) annotated on the page that answers the question and one or more short spans from the annotated passage containing the actual answer. The long and the short answer annotations can however be empty. If they are both empty, then there is no answer on the page at all. If the long answer annotation is non-empty, but the short answer annotation is empty, then the annotated passage answers the question but no explicit short answer could be found. Finally 1% of the documents have a passage annotated with a short answer that is “yes” or “no”, instead of a list of short spans. </p> <p class="description-stats"> 1,204 <span class="smaller-text">PAPERS</span> • 10 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/carla"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003534-359924c7_16ZetSd.gif');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> CARLA <span class="full-name">(Car Learning to Act)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003534-8b6e0880.gif"> <p> CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation). </p> <p class="description-stats"> 1,189 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/oxford-102-flower"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000006146-fae85810_bWIHalt.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Oxford 102 Flower <span class="full-name">(102 Category Flower Dataset)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000006146-a61d61d7.jpg"> <p> Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. </p> <p class="description-stats"> 1,179 <span class="smaller-text">PAPERS</span> • 17 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/movielens"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000331-256e4bc5_9wkAHjr.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MovieLens </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000331-ed9b41de.jpg"> <p> The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations. </p> <p class="description-stats"> 1,165 <span class="smaller-text">PAPERS</span> • 17 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/gsm8k"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/f605201d-376b-4ce9-9b97-3a7913a63b96.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> GSM8K </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/8e5b1a3e-a72f-49bd-875d-99cb2ac66607.jpg"> <p> GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. </p> <p class="description-stats"> 1,164 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/pubmed"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-color: #90B06D;opacity: 0.3;"> <span class="dataset-img-icon"><span class=" icon-wrapper icon-fa icon-fa-duotone" data-name=""></span></span> <span class="dataset-img-text">Pubmed</span> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Pubmed </span> <div class="description"> <p> The PubMed dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. </p> <p class="description-stats"> 1,158 <span class="smaller-text">PAPERS</span> • 24 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/qnli"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001007-59988d2a_tmh6oda.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> QNLI <span class="full-name">(Question-answering NLI)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001007-945d04a4.jpg"> <p> The QNLI (Question-answering NLI) dataset is a Natural Language Inference dataset automatically derived from the Stanford Question Answering Dataset v1.1 (SQuAD). SQuAD v1.1 consists of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The dataset was converted into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue. The QNLI dataset is part of GLUE benchmark. </p> <p class="description-stats"> 1,148 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/ade20k"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000451-9a281a33_PVzHRmP.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> ADE20K </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000451-1e601a44.jpg"> <p> The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. </p> <p class="description-stats"> 1,116 <span class="smaller-text">PAPERS</span> • 28 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/places"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003545-60d93b9a_7dnEN4W.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Places </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000003545-58909355.jpg"> <p> The Places dataset is proposed for scene recognition and contains more than 2.5 million images covering more than 205 scene categories with more than 5,000 images per category. </p> <p class="description-stats"> 1,105 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/tiny-imagenet"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000001404-8b127808_sBRZER5.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Tiny ImageNet </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000001404-50df3ae0.jpg"> <p> 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. </p> <p class="description-stats"> 1,094 <span class="smaller-text">PAPERS</span> • 8 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/stl-10"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000425-d70e0e76_ccGeTv1.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> STL-10 <span class="full-name">(Self-Taught Learning 10)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000425-ecbfc3ff.jpg"> <p> The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size. </p> <p class="description-stats"> 1,043 <span class="smaller-text">PAPERS</span> • 18 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/office-home"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000862-18b92295_QcdVuiG.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Office-Home </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000862-cf01d085.jpg"> <p> Office-Home is a benchmark dataset for domain adaptation which contains 4 domains where each domain consists of 65 categories. The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.; Clipart – collection of clipart images; Product – images of objects without a background and Real-World – images of objects captured with a regular camera. It contains 15,500 images, with an average of around 70 images per class and a maximum of 99 images in a class. </p> <p class="description-stats"> 1,012 <span class="smaller-text">PAPERS</span> • 12 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/penn-treebank"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000009-f8841951_mDfJdwW.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> Penn Treebank </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000009-df5bf484.jpg"> <p> The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating each word with its Part-of-Speech tag. In the most common split of this corpus, sections from 0 to 18 are used for training (38 219 sentences, 912 344 tokens), sections from 19 to 21 are used for validation (5 527 sentences, 131 768 tokens), and sections from 22 to 24 are used for testing (5 462 sentences, 129 654 tokens). The corpus is also commonly used for character-level and word-level Language Modelling. </p> <p class="description-stats"> 991 <span class="smaller-text">PAPERS</span> • 10 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/mimic-iii"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/5065d19d-ca94-46cf-a655-3a464647b0ed.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> MIMIC-III <span class="full-name">(The Medical Information Mart for Intensive Care III)</span> </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/ec05fd66-83ae-4c94-aac6-bccace3d847f.jpg"> <p> The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. </p> <p class="description-stats"> 972 <span class="smaller-text">PAPERS</span> • 7 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> <div class="dataset-wide-box"> <a href="/dataset/wikitext-2"> <div class="row align-items-center justify-content-center"> <div class="col-6 col-sm-2 item-image"> <div class="card-background-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000035-5a487425_S43AtK7.jpg');"> </div> </div> <div class="col-sm-10 dataset-details"> <span class="name"> WikiText-2 </span> <div class="description"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset-medium/dataset-0000000035-8284831e.jpg"> <p> The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. </p> <p class="description-stats"> 954 <span class="smaller-text">PAPERS</span> • 4 <span class="smaller-text">BENCHMARKS</span> </p> </div> </div> </div> </a> </div> </div> <nav class="datasets-nav" aria-label="Page navigation"> <ul class="pagination justify-content-center d-none d-sm-flex"> <li class="page-item disabled"> <a class="page-link" href="#" aria-label="Previous"> <span aria-hidden="true"><span class=" icon-wrapper icon-fa icon-fa-solid" data-name="angle-left"><svg viewBox="0 0 256 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M31.7 240.998l136-136c9.4-9.4 24.6-9.4 33.9 0l22.6 22.6c9.4 9.4 9.4 24.6 0 33.9l-96.3 96.5 96.4 96.4c9.4 9.4 9.4 24.6 0 33.9l-22.6 22.7c-9.4 9.4-24.6 9.4-33.9 0l-136-136c-9.5-9.4-9.5-24.6-.1-34z"/></svg></span></span> <span class="sr-only">Previous</span> </a> </li> <li class="page-item disabled"><a class="page-link" href="#">1</a></li> <li class="page-item"> <a 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