Group Normalization (GN) is introduced as an alternative to Batch Normalization (BN) in deep learning. BN has been widely used but suffers from performance degradation when batch sizes are small due to inaccurate batch statistics estimation. GN addresses this issue by dividing channels into groups and normalizing within each group, making its computation independent of batch size and maintaining stable accuracy across various batch sizes. GN outperforms BN in tasks such as image classification, object detection, and video classification, and can be easily implemented in modern deep learning frameworks. GN is particularly effective in scenarios where small batch sizes are necessary due to memory constraints, such as in computer vision tasks like detection, segmentation, and video recognition. It can be naturally transferred from pre-training to fine-tuning, and performs well in these tasks. GN is also compatible with sequential and generative models, although this is beyond the scope of this paper. GN is compared with other normalization methods such as Layer Normalization (LN) and Instance Normalization (IN). While LN and IN are also batch-independent, they do not achieve the same level of accuracy as GN in visual recognition tasks. GN's performance is validated through extensive experiments on datasets such as ImageNet, COCO, and Kinetics, demonstrating its effectiveness as a competitive alternative to BN. GN is implemented with a few lines of code in frameworks like PyTorch and TensorFlow, and is shown to be effective in various deep learning applications. The results indicate that GN provides stable and accurate performance across different batch sizes, making it a valuable tool for deep learning models that require small batch sizes.Group Normalization (GN) is introduced as an alternative to Batch Normalization (BN) in deep learning. BN has been widely used but suffers from performance degradation when batch sizes are small due to inaccurate batch statistics estimation. GN addresses this issue by dividing channels into groups and normalizing within each group, making its computation independent of batch size and maintaining stable accuracy across various batch sizes. GN outperforms BN in tasks such as image classification, object detection, and video classification, and can be easily implemented in modern deep learning frameworks. GN is particularly effective in scenarios where small batch sizes are necessary due to memory constraints, such as in computer vision tasks like detection, segmentation, and video recognition. It can be naturally transferred from pre-training to fine-tuning, and performs well in these tasks. GN is also compatible with sequential and generative models, although this is beyond the scope of this paper. GN is compared with other normalization methods such as Layer Normalization (LN) and Instance Normalization (IN). While LN and IN are also batch-independent, they do not achieve the same level of accuracy as GN in visual recognition tasks. GN's performance is validated through extensive experiments on datasets such as ImageNet, COCO, and Kinetics, demonstrating its effectiveness as a competitive alternative to BN. GN is implemented with a few lines of code in frameworks like PyTorch and TensorFlow, and is shown to be effective in various deep learning applications. The results indicate that GN provides stable and accurate performance across different batch sizes, making it a valuable tool for deep learning models that require small batch sizes.