Transfer Learning for small datasets in Weed Identification

This seminar explores how transfer learning can accelerate weed identification in agriculture using limited training data. Instead of training models from scratch, participants learn to adapt pre-trained vision models (CNNs and Vision Transformers) to agricultural tasks by fine-tuning only select layers. This approach reduces data needs and improves accuracy by leveraging existing visual knowledge.
Through hands-on PyTorch labs, the session guides users in fine-tuning both a CNN and a Vision Transformer on crop-health images, evaluating performance and adjusting key hyperparameters.