Content-based commodity retrieval (CCR) faces two major challenges: (1) commodities in real-world scenarios are often captured randomly by users, resulting in significant variations in image backgrounds, poses, shooting angles, and brightness; and (2) many commodities in the CCR dataset have similar appearances but belong to different brands or distinct products within the same brand. We introduce a CCR neural network called CCR-Net, which incorporates both length loss and salient loss. These two losses can operate independently or collaboratively to enhance retrieval quality. CCR-Net offers several advantages, including the ability to (1) minimize data variations in real-world captured images; and (2) differentiate between images containing highly similar but fundamentally distinct commodities, resulting in improved commodity retrieval capabilities. Comprehensive experiments demonstrate that our CCR-Net achieves state-of-the-art performance on the CUB200-2011, Perfect500k, and Stanford Online Products datasets for commodity retrieval tasks.