Beginner Transfer Learning Lab with MobileNet and ResNet

Training an image classifier from scratch requires data and compute that beginners rarely have, which is why transfer learning, reusing networks pre-trained on millions of images, is the technique that actually unlocks practical computer vision for newcomers. Yet the concept stays abstract until a learner sees a pre-trained model classify their own photographs correctly. The intern builds a Streamlit application that makes transfer learning tangible: users upload any image and classify it using pre-trained MobileNet or ResNet models loaded through TensorFlow. The app displays the top predicted classes with confidence scores and lets users switch between the two architectures on the same image to compare their answers and inference speed. The intern implements the specific preprocessing each network expects, presents results clearly, and adds explanatory content describing what the models were trained on and why their knowledge transfers to new images, turning the app into a genuine self-serve lesson. Completing the lab gives the intern a working understanding of pre-trained architectures and the trade-off between lightweight and heavyweight models, practical TensorFlow inference skills, and a deployed Python application demonstrating they can make advanced ideas accessible, a foundation for the fine-tuning projects that come next.