CVNets: High Performance Library for Computer Vision
By Sachin Mehta, Farzad Abdolhosseini, Mohammad Rastegari. 2022
30 citations recorded by the source corpus.
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: https://github.com/apple/ml-cvnets.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, ML Systems & Hardware
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohammad Rastegari
- Sachin Mehta
- Oncel Tuzel
- Hadi Pouransari
- Ali Farhadi
- Mehrdad Farajtabar
- Fartash Faghri
- Dacheng Tao
In Computer Vision and Pattern Recognition
- Mohammad Rastegari
- Sachin Mehta
- Oncel Tuzel
- Luc Van Gool
- Xiaogang Wang
- Ali Farhadi
- Dacheng Tao
- Ming-Hsuan Yang
In ML Systems & Hardware
Citations in the corpus
- Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement (2023, methodology): “We follow [42, 64] and use state-of-the-art recipes, including optimizers, hyperpa-rameters, and learning schedules, specific to each model on the ImageNet.”
- Separable Self-attention for Mobile Vision Transformers (2022, methodology): “We implement our models using CVNets [4, 32], and use their provided scripts for data processing, training, and evaluation.”