{"url":"/method/shufflenet","slug":"shufflenet","name":"ShuffleNet","full_name":"ShuffleNet","full_name_withheld":false,"description_markdown":"**ShuffleNet** is a convolutional neural network designed specially for mobile devices with very limited computing power. The architecture utilizes two new operations, pointwise group [convolution](https://paperswithcode.com/method/convolution) and [channel shuffle](https://paperswithcode.com/method/channel-shuffle), to reduce computation cost while maintaining accuracy.","description_state":"present","introduced_year":null,"introduced_by":{"title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","paper":"/paper/shufflenet-an-extremely-efficient","first_author":"Xiangyu Zhang","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/shufflenet-an-extremely-efficient"},"source":{"url":"http://arxiv.org/abs/1707.01083v2","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/shufflenetv1.py","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Light-weight neural networks","url":"/methods/category/light-weight-neural-networks","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural Networks","url":"/methods/category/convolutional-neural-networks","pwc_aliases":[]}],"n_papers_tagged":51,"archive_num_papers":51,"papers_newest_first":[{"paper":null,"title":"Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection","date":"2025-06-14","arxiv_id":"2506.14834","n_code_links":0,"syntology":null},{"paper":null,"title":"Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection","date":"2025-01-08","arxiv_id":"2501.04196","n_code_links":0,"syntology":null},{"paper":null,"title":"Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification","date":"2024-08-03","arxiv_id":"2408.01752","n_code_links":0,"syntology":null},{"paper":null,"title":"Fragility, Robustness and Antifragility in Deep Learning","date":"2023-12-15","arxiv_id":"2312.09821","n_code_links":0,"syntology":null},{"paper":null,"title":"Generalizability of CNN Architectures for Face Morph Presentation Attack","date":"2023-10-17","arxiv_id":"2310.11105","n_code_links":0,"syntology":null},{"paper":null,"title":"A Non-monotonic Smooth Activation Function","date":"2023-10-16","arxiv_id":"2310.10126","n_code_links":0,"syntology":null},{"paper":null,"title":"Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response","date":"2023-08-29","arxiv_id":"2308.16203","n_code_links":0,"syntology":null},{"paper":null,"title":"PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks","date":"2023-03-07","arxiv_id":"2303.04269","n_code_links":0,"syntology":null},{"paper":null,"title":"Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification","date":"2023-02-22","arxiv_id":"2302.11093","n_code_links":0,"syntology":null},{"paper":null,"title":"QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning","date":"2023-02-01","arxiv_id":"2302.00252","n_code_links":0,"syntology":null},{"paper":null,"title":"Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer","date":"2022-08-22","arxiv_id":"2208.10495","n_code_links":0,"syntology":null},{"paper":"/paper/design-and-analysis-of-novel-bit-flip-attacks","title":"Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs","date":"2022-06-24","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"Structured Pruning is All You Need for Pruning CNNs at Initialization","date":"2022-03-04","arxiv_id":"2203.02549","n_code_links":0,"syntology":null},{"paper":null,"title":"Multimodal registration of FISH and nanoSIMS images using convolutional neural network models","date":"2022-01-14","arxiv_id":"2201.05545","n_code_links":0,"syntology":null},{"paper":"/paper/threshnet-an-efficient-densenet-using","title":"ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections","date":"2022-01-09","arxiv_id":"2201.03013","n_code_links":1,"syntology":null},{"paper":null,"title":"Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique","date":"2022-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/smu-smooth-activation-function-for-deep","title":"SMU: smooth activation function for deep networks using smoothing maximum technique","date":"2021-11-08","arxiv_id":"2111.04682","n_code_links":6,"syntology":null},{"paper":null,"title":"Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"SAU: Smooth activation function using convolution with approximate identities","date":"2021-09-27","arxiv_id":"2109.13210","n_code_links":0,"syntology":null},{"paper":null,"title":"ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions","date":"2021-09-09","arxiv_id":"2109.04386","n_code_links":0,"syntology":null},{"paper":null,"title":"High performing ensemble of convolutional neural networks for insect pest image detection","date":"2021-08-28","arxiv_id":"2108.12539","n_code_links":0,"syntology":null},{"paper":null,"title":"Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System","date":"2021-08-01","arxiv_id":"2108.00392","n_code_links":0,"syntology":null},{"paper":null,"title":"Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework","date":"2021-06-16","arxiv_id":"2106.09121","n_code_links":0,"syntology":null},{"paper":"/paper/lite-hrnet-a-lightweight-high-resolution","title":"Lite-HRNet: A Lightweight High-Resolution Network","date":"2021-04-13","arxiv_id":"2104.06403","n_code_links":17,"syntology":{"ran":12,"of":29,"unverified":17,"pointer_only":13}},{"paper":null,"title":"ENOS: Energy-Aware Network Operator Search for Hybrid Digital and Compute-in-Memory DNN Accelerators","date":"2021-04-12","arxiv_id":"2104.05217","n_code_links":0,"syntology":null},{"paper":"/paper/gnn-rl-compression-topology-aware-network","title":"Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning","date":"2021-02-05","arxiv_id":"2102.03214","n_code_links":1,"syntology":{"ran":3,"of":4,"unverified":1,"pointer_only":4}},{"paper":"/paper/kaleidoscope-an-efficient-learnable-1","title":"Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps","date":"2020-12-29","arxiv_id":"2012.14966","n_code_links":2,"syntology":{"ran":16,"of":23,"unverified":7,"pointer_only":0}},{"paper":"/paper/ensemble-cvdnet-a-deep-learning-based-end-to","title":"Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks","date":"2020-12-09","arxiv_id":"2012.09132","n_code_links":1,"syntology":null},{"paper":"/paper/deep-transfer-learning-for-automated","title":"Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs","date":"2020-11-06","arxiv_id":"2011.04475","n_code_links":1,"syntology":null},{"paper":"/paper/mimicnorm-weight-mean-and-last-bn-layer-mimic","title":"MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization","date":"2020-10-19","arxiv_id":"2010.09278","n_code_links":1,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":6},{"task":"/task/deep-learning","name":"Deep Learning","papers":5},{"task":"/task/object-detection","name":"Object Detection","papers":5},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":5},{"task":"/task/image-classification","name":"image-classification","papers":5},{"task":null,"name":"GPU","papers":4},{"task":"/task/model-compression","name":"Model Compression","papers":4},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":4},{"task":"/task/object-detection-1","name":"object-detection","papers":4},{"task":"/task/diagnostic","name":"Diagnostic","papers":3},{"task":"/task/real-time-semantic-segmentation","name":"Real-Time Semantic Segmentation","papers":3},{"task":"/task/all","name":"All","papers":2},{"task":"/task/computational-efficiency","name":"Computational Efficiency","papers":2},{"task":"/task/efficient-neural-network","name":"Efficient Neural Network","papers":2},{"task":"/task/classification","name":"General Classification","papers":2},{"task":"/task/network-pruning","name":"Network Pruning","papers":2},{"task":"/task/neural-network-compression","name":"Neural Network Compression","papers":2},{"task":"/task/quantization","name":"Quantization","papers":2},{"task":"/task/rain-removal","name":"Rain Removal","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2}],"tasks_shown":20,"n_tasks":57,"usage_by_year":[{"year":"2017","papers":1},{"year":"2018","papers":8},{"year":"2019","papers":7},{"year":"2020","papers":9},{"year":"2021","papers":10},{"year":"2022","papers":6},{"year":"2023","papers":7},{"year":"2024","papers":1},{"year":"2025","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/shufflenet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}