ResNet-101 is a deep convolutional neural network in the ResNet (Residual Network) series, introduced by Kaiming He and his team in 2015. ResNet-101 consists of 101 layers and utilizes residual connections (skip connections) to address the vanishing gradient problem in deep networks, allowing it to train very deep structures without loss of accuracy. These residual connections let input features be directly passed to subsequent layers, simplifying training and enhancing model performance. ResNet-101 performs excellently in tasks such as image classification, object detection, and semantic segmentation, with its depth making it suitable for complex tasks requiring high-level feature representation. Despite its larger parameter count, its high accuracy and strong transferability have led to its widespread use in computer vision applications.
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