Data to 7 Oct 2026

Caffe: Convolutional Architecture for Fast Feature Embedding

arXiv 1408.5093 ↗First posted 20 Jun 2014cs.CV cs.LG cs.NEcited by 287

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Abstract (arXiv)

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (≈ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

Key facts. Cited by 287 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2014.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 90; 2022: 82; 2023: 59; 2024: 31; 2025: 14; 2026: 10.

287 papers.

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2021Merging Models with Fisher-Weighted Averagingcited by 613
2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
2024TextGrad: Automatic "Differentiation" via Textcited by 250
2021Anabranch Network for Camouflaged Object Segmentationcited by 227
2021Scaling Local Self-Attention for Parameter Efficient Visual Backbonescited by 220
2023Large Language Models in Law: A Surveycited by 128
2021Avalanche: an End-to-End Library for Continual Learningcited by 124
2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
2021SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Networkcited by 83
2023Full Stack Optimization of Transformer Inference: a Surveycited by 79
2021CBNet: A Composite Backbone Network Architecture for Object Detectioncited by 77

38 more

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2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
2021Rethinking "Batch" in BatchNormcited by 44
2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
2021Solving Inefficiency of Self-supervised Representation Learningcited by 41
2021Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloadscited by 40
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2021A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detectioncited by 34
2022SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classificationcited by 34
2024Spatiotemporal Skip Guidance for Enhanced Video Diffusion Samplingcited by 33
2022Flashlight: Enabling Innovation in Tools for Machine Learningcited by 25
2022Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspacescited by 25
2022Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domaincited by 22
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2023Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruningcited by 16
2024RetinaGS: Scalable Training for Dense Scene Rendering with Billion-Scale 3D Gaussianscited by 16
2021Clairvoyant Prefetching for Distributed Machine Learning I/Ocited by 14
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2021Launchpad: A Programming Model for Distributed Machine Learning Researchcited by 14
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2023Tensor Decomposition for Model Reduction in Neural Networks: A Reviewcited by 14
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2021LazyTensor: combining eager execution with domain-specific compilerscited by 11
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2021Inferring Latent Domains for Unsupervised Deep Domain Adaptationcited by 11

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