Pretrained models usually fail in agricultural environments. Fine-tuning on domain-specific field data and deploying to edge hardware is the only architecture that works for high-precision production robotics. In this article, we discuss the trade-offs, performance, and advocate the "why" behind fine-tuning custom vision models for your agriculture use case.

Learn how to train Qwen2.5-VL to automatically detect and describe objects in images. This guide covers dataset preparation, training on consumer GPUs, and real-world results with detailed examples and troubleshooting tips

Datature now supports Multilabel Classification, allowing models to assign multiple independent labels to a single image using binary cross entropy with logits instead of softmax-based training. With co-occurrence matrices, streamlined workflows, and flexible deployment options, teams can build and deploy richer, multi-attribute vision systems for real-world use cases.