A complete image-classification pipeline in Python has to be built, trained, and tuned so it performs reliably on everyday, real-world photos. The core work covers data handling, model design, training, evaluation, and result optimisation—whichever framework you prefer (TensorFlow, PyTorch, or Keras) is fine as long as the final code is clean, well-commented, and reproducible. I have the image data ready and will share it once we start. The goal is to squeeze the best possible accuracy out of it while keeping the model lightweight enough for practical deployment. I am particularly interested in people who can point to solid, relevant experience on similar classification tasks; a brief note on past results or published repositories is more valuable to me than a long formal proposal. Optimzation more than the current trxhniquea of nerf ( neural radiance fields ) Deliverables • Well-structured Python project (scripts or notebook) that trains the classifier • Saved model weights and label mapping • Short README explaining environment setup, training steps, and how to run inference • Summary report highlighting accuracy, confusion matrix, and any optimisation techniques used (augmentation, fine-tuning, pruning, etc.) Acceptance Criteria A minimum test-set accuracy agreed upon at project start, code that runs end-to-end on my machine without modification, and concise documentation are the measures for sign-off.