Deploying deep learning on nano–drone platforms imposes strict constraints on latency, memory, and energy. This thesis investigates the use of dynamic inference solutions to tackle this problem, focusing in particular on object detection, a common task in many applications such as navigation, obstacle avoidance, search and rescue, etc. The proposed approach modifies a MobileNetV2 SSDLite (input 512×512) into a so-called slimmable model, where four width configurations (0.25×, 0.5×, 0.75×, 1.0×) can be dynamically selected for layers after the 6th feature extractor block. The four widths share a single set of weights, except for width-private batch normalization statistics, thus incurring a minimal memory overhead with respect to the original model.