mlsanityData to 7 Oct 2026

CVNets: High Performance Library for Computer Vision

arXiv 2206.02002 ↗First posted 4 Jun 2022cs.CV cs.LGcited by 16

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Abstract (arXiv)

We introduce CVNets, a high-performance open-source library for training deep neural networks for visual recognition tasks, including classification, detection, and segmentation. CVNets supports image and video understanding tools, including data loading, data transformations, novel data sampling methods, and implementations of several standard networks with similar or better performance than previous studies. Our source code is available at: urlhttps://github.com/apple/ml-cvnets.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 4 Jun 2022.

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20 of 27 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  8. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  9. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
  10. 2019Searching for MobileNetV3cited by 1,984
  11. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  12. 2017Random Erasing Data Augmentationcited by 1,235
8 more
  1. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
  2. 2021ResNet strikes back: An improved training procedure in timmcited by 365
  3. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 269
  4. 2020GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learningcited by 224
  5. 2019Fixing the train-test resolution discrepancycited by 209
  6. 2017DSSD : Deconvolutional Single Shot Detectorcited by 130
  7. 2019Augment your batch: better training with larger batchescited by 37
  8. 2021PyTorchVideo: A Deep Learning Library for Video Understandingcited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 6; 2025: 2; 2026: 1.

16 papers.

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  1. 2022Separable Self-attention for Mobile Vision Transformerscited by 163
  2. 2023SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understandingcited by 111
  3. 2024OpenELM: An Efficient Language Model Family with Open Training and Inference Frameworkcited by 74
  4. 2024Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculumcited by 25
  5. 2023Bytes Are All You Need: Transformers Operating Directly On File Bytescited by 16
  6. 2023Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcementcited by 11
  7. 2022RangeAugment: Efficient Online Augmentation with Range Learningcited by 8
  8. 2025Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferencescited by 7
  9. 2024TorchOpera: A Compound AI System for LLM Safetycited by 4
  10. 2024Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancementcited by 3
  11. 2023Diffusion Models as Masked Audio-Video Learnerscited by 2
  12. 2024CtrlSynth: Controllable Image Text Synthesis for Data-Efficient Multimodal Learningcited by 2
4 more
  1. 2024Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifiercited by 2
  2. 2023On the Efficacy of Multi-scale Data Samplers for Vision Applicationscited by 1
  3. 2025QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networkscited by 0
  4. 2026Human-inspired Global-to-Parallel Multi-scale Encoding for Lightweight Vision Modelscited by 0