Title: FOC OSOD: Focus on Classification One-Shot Object Detection. Abstract: One-shot object detection (OSOD) aims at detecting all instances that are consistent with the category of the single reference image. OSOD achieves object detection by comparing the query image and the reference image. We observe that the essential problem behind the limited performance of OSOD is that OSOD generates a lot of false positives due to its poor classification ability. This paper analyzes the serious false positive problem in OSOD and proposes a Focus on Classification One-Shot Object Detection (FOC OSOD) framework, which is improved in two important aspects: (1) classification cascade head with the fixed IoU threshold can enhance the robustness of classification by comparing multiple close regions; (2) classification region deformation on the query feature and the reference feature to obtain a more effective comparison region. Without bells and whistles, a single FOC obtains 1.8% AP and 1.3% AP improvement on the seen classes and the unseen classes over a Siamese Faster R-CNN baseline on the MS-COCO dataset in the one-shot setting. The code will be available.