{"url":"/dataset/fer2013","name":"FER2013","full_name":"Facial Expression Recognition 2013 Dataset","description_markdown":"Fer2013 contains approximately 30,000 facial RGB images of different expressions with size restricted to 48×48, and the main labels of it can be divided into 7 types: 0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral. The Disgust expression has the minimal number of images – 600, while other labels have nearly 5,000 samples each.\r\n\r\nSource: [Eavesdrop the Composition Proportion of Training Labels in Federated Learning](https://arxiv.org/abs/1910.06044)\r\nImage Source: [https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-3-fer2013-c38f2e052280](https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-3-fer2013-c38f2e052280)","description_withheld":null,"homepage":"https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/challenges-in-representation-learning-a","title":"Challenges in Representation Learning: A report on three machine learning contests","first_author":"Ian J. Goodfellow","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Emotion Recognition","url":"/task/emotion-recognition","datasets_with_task":"/datasets/task/emotion-recognition"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","datasets_with_task":"/datasets/task/facial-expression-recognition"},{"name":"Image Compression","url":"/task/image-compression","datasets_with_task":"/datasets/task/image-compression"},{"name":"Facial Expression Recognition","url":"/task/facial-expression-recognition-1","datasets_with_task":"/datasets/task/facial-expression-recognition-1"}],"languages":[],"variants":["FER2013"],"data_loaders":[{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.FER2013.html","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/fer2013-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":168,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-fer2013","task":"Facial Expression Recognition (FER)","dataset_variant":"FER2013","rows":17,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"EfficientFER","paper":"/paper/efficientfer-efficientnetv2-based-deep","metrics":{"Accuracy":"82.47"},"code_links":[{"title":"mkonuk66/EfficientFER","url":"https://github.com/mkonuk66/EfficientFER"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-expression-recognition-on-fer2013-1","task":"Facial Expression Recognition","dataset_variant":"FER2013","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VGG","paper":"/paper/facial-expression-recognition-using","metrics":{"Accuracy":"72.7"},"code_links":[{"title":"amilkh/cs230-fer","url":"https://github.com/amilkh/cs230-fer"},{"title":"pranjalrai-iitd/FER2013-Facial-Emotion-Recognition-","url":"https://github.com/pranjalrai-iitd/FER2013-Facial-Emotion-Recognition-"},{"title":"apuayush/face_express","url":"https://github.com/apuayush/face_express"},{"title":"janZub-AI/EmotionDetectionVGG","url":"https://github.com/janZub-AI/EmotionDetectionVGG"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/emotion-recognition-on-fer2013-1","task":"Emotion Recognition","dataset_variant":"FER2013","rows":1,"metrics":["5-class test accuracy"],"first_row_in_archive_order":{"model":"VGG based","paper":"/paper/identiface-a-vgg-based-multimodal-facial","metrics":{"5-class test accuracy":"66.13%"},"code_links":[{"title":"MahmoudRabea13/IdentiFace","url":"https://github.com/MahmoudRabea13/IdentiFace"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-fer2013","task":"Image Clustering","dataset_variant":"FER2013","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TURTLE (CLIP + DINOv2)","paper":"/paper/let-go-of-your-labels-with-unsupervised-1","metrics":{"Accuracy":"36.2"},"code_links":[{"title":"mlbio-epfl/turtle","url":"https://github.com/mlbio-epfl/turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-compression-on-fer2013","task":"Image Compression","dataset_variant":"FER2013","rows":1,"metrics":["10%"],"first_row_in_archive_order":{"model":"SUDHEER","paper":"/paper/deep-convolutional-neural-network-for-image","metrics":{"10%":"1"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficientfer-efficientnetv2-based-deep","title":"EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition","date":"2025-06-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sdafe-a-dual-filter-stable-diffusion-data","title":"SDAFE: A Dual-filter Stable Diffusion Data Augmentation Method for Facial Expression Recognition","date":"2025-04-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mini-resemotenet-leveraging-knowledge","title":"Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design","date":"2025-01-30","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/emonext-an-adapted-convnext-for-facial-1","title":"EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition","date":"2025-01-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/resemotenet-bridging-accuracy-and-loss","title":"ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition","date":"2024-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/identiface-a-vgg-based-multimodal-facial","title":"IdentiFace : A VGG Based Multimodal Facial Biometric System","date":"2024-01-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-novel-facial-emotion-recognition-model","title":"A novel facial emotion recognition model using segmentation VGG-19 architecture","date":"2023-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ad-corre-adaptive-correlation-based-loss-for","title":"Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild","date":"2022-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fer2013-recognition-resnet18-with-tricks","title":"Fer2013 Recognition - ResNet18 With Tricks","date":"2021-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/local-multi-head-channel-self-attention-for","title":"Local Multi-Head Channel Self-Attention for Facial Expression Recognition","date":"2021-11-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/facial-emotion-recognition-state-of-the-art","title":"Facial Emotion Recognition: State of the Art Performance on FER2013","date":"2021-05-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/facial-expression-recognition-using-residual","title":"Facial Expression Recognition using Residual Masking Network","date":"2021-05-05","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-network-hyperparameters","title":"Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition","date":"2021-03-25","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-emotion-facial-expression-recognition","title":"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network","date":"2019-02-04","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/local-learning-with-deep-and-handcrafted","title":"Local Learning with Deep and Handcrafted Features for Facial Expression Recognition","date":"2018-04-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/facial-expression-recognition-using","title":"Facial Expression Recognition using Convolutional Neural Networks: State of the Art","date":"2016-12-09","rows_on_this_dataset":3,"code_links":4,"syntology":null},{"paper":"/paper/deep-convolutional-neural-network-for-image","title":"Deep Convolutional Neural Network for Image Deconvolution","date":"2014-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/challenges-in-representation-learning-a","title":"Challenges in Representation Learning: A report on three machine learning contests","date":"2013-07-01","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":16,"samples_ran":3,"samples_unverified":13,"pointer_only_for_licence":4,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}