I need a hand completing an academic assignment that moves through three clear stages. First, I’ll share a small image dataset that must be meticulously annotated in Fiji using the Labkit plug-in. Once the labels are in place, we’ll shift into TensorFlow to build a clean, well-documented classification model that trains reliably on the freshly annotated data. The final step is packaging everything—weights, code, and a short “how-to-run” note—so the model can be submitted and tested without tweaks. Key deliverables: • Labkit-compatible annotation files plus the updated dataset • Reproducible TensorFlow training and inference scripts • Trained classification model ready for submission/testing, with a concise README I’ll provide the raw images, any existing label guidelines, and target evaluation criteria as soon as we kick off. Let me know your Labkit experience and usual turnaround time so we can get started right away.