MobileNetV2-Based Deep Learning Model for Sports Image Classification

Description:

The MobileNetV2-Based Deep Learning Model for Sports Image Classification is an intelligent computer vision system that can automatically recognize different types of sports from images. It is built on the MobileNetV2 architecture — a lightweight and highly efficient deep learning model known for its speed and accuracy. MobileNetV2 uses depth wise separable convolutions and an inverted residual structure, which reduces the number of parameters and computations without losing performance.

This model is especially useful in systems, where users can simply upload an image, and the model instantly predicts which sport it belongs to. It can identify sports such as football, basketball, tennis, cricket, swimming, and athletics, among many others.

Because of its compact size and high efficiency, the model can run smoothly even on limited hardware, making it ideal for real-time sports recognition and AI-powered assistants.

Input:

1. Upload a sports image

Output:

1. The name and about of sport detected in the image

Tags:

#MobileNetV2 #DeepLearning #SportsClassification #AIinSports #ImageRecognition #MachineLearning #TransferLearning #ComputerVision #SmartAI #RealTimeAnalysis #TensorFlow #Keras #WebIntegration #LightweightModel #SportsTechnology #AIChat #EdgeAI #VisualRecognition

References:

1. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). This paper introduced MobileNetV2, a highly efficient model architecture that uses depthwise separable convolutions to make neural networks faster and lighter while maintaining strong accuracy.

2. Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., & Le, Q. V. (2019). Searching for MobileNetV3. Proceedings of the IEEE International Conference on Computer Vision (ICCV). This work expanded on the MobileNet family, introducing new architectural improvements for mobile-friendly AI models and inspiring similar approaches for efficient visual recognition tasks.

3. He, K., Zhang, X., Ren, S., & Sun, J (2016). Deep Residual Learning for Image Recognition.CVPR. This foundational study introduced residual networks (ResNet), which inspired modern CNN designs, including the efficient block structures used in MobileNetV2.