  Crowd counting finds direct applications in real-world situations, making
computational efficiency and performance crucial. However, most of the previous
methods rely on a heavy backbone and a complex downstream architecture that
restricts the deployment. To address this challenge and enhance the versatility
of crowd-counting models, we introduce two lightweight models. These models
maintain the same downstream architecture while incorporating two distinct
backbones: MobileNet and MobileViT. We leverage Adjacent Feature Fusion to
extract diverse scale features from a Pre-Trained Model (PTM) and subsequently
combine these features seamlessly. This approach empowers our models to achieve
improved performance while maintaining a compact and efficient design. With the
comparison of our proposed models with previously available state-of-the-art
(SOTA) methods on ShanghaiTech-A ShanghaiTech-B and UCF-CC-50 dataset, it
achieves comparable results while being the most computationally efficient
model. Finally, we present a comparative study, an extensive ablation study,
along with pruning to show the effectiveness of our models.
