CondenseNet and CondenseNetV2
About CondenseNet
ShichenLiu/CondenseNet
CondenseNet: Light weighted CNN for mobile devices
This project offers a method to build highly efficient image recognition models that can run on devices with limited computational power, such as mobile phones or embedded systems. It takes raw image data and processes it into classification results, such as identifying objects or faces. This is for engineers or product managers who need to deploy robust image recognition features in resource-constrained environments.
About CondenseNetV2
jianghaojun/CondenseNetV2
[CVPR 2021] CondenseNet V2: Sparse Feature Reactivation for Deep Networks
This project offers an improved deep learning model for computer vision tasks like image classification and object detection. It takes raw image datasets (e.g., ImageNet, COCO) and outputs highly accurate, yet computationally efficient, trained models. Researchers and practitioners working on vision AI applications will find this useful for developing high-performance image analysis systems.
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