How to Train MobileNetV2 On a Custom Dataset

The blog post by Samrat Sahoo provides a detailed tutorial on training the MobileNetV2 classification model, which was developed by Google, to recognize custom image datasets using transfer learning from ImageNet. The process involves using Roboflow to download and manage datasets, converting these into a Tensorflow ImageFolder format, and leveraging TensorFlow to build and train the MobileNetV2 model, which uses an inverted residual structure for improved performance. The tutorial explains the steps for constructing and fine-tuning the model, including freezing certain layers to optimize accuracy and using a Global Average Pooling 2D layer for predictions. Roboflow is highlighted as a tool for easily managing datasets and deploying models, and readers are encouraged to explore the full code in a Colab notebook for practical implementation.