In this paper, we present our solution for tiger detection in the 2019 Computer Vision for Wildlife Conservation Challenge (CVWC2019). We introduce an efficient deep tiger detector, which consists of the convnet channel adaptation method and an improved tiger detection method based on You Only Look Once version 3 (YOLOv3). Considering the limited memory and computing power of tiny embedded devices, we have used EfficientNet-B0 and Darknet-53 as backbone networks for detection and adapted them to balance their depth and width inspired by the channel pruning method and knowledge distillation method. Our results show that after an architecture adjustment of Darknet-53, the floating-point computation decreases by 93%, its model size decreases by 97%, and its accuracy only decreases by 1%; after an architecture adjustment of EfficientNet-B0, the floating-point computation decreases by 66%, its model size decreases by 70% with its accuracy only decreased by 1%. We also compare GIoU loss and MSE loss in the training stage. The GIoU loss has the advantage that it increases the average AP for IoU from 0.5 to 0.95 without affecting training speed and the interface speed, so it is experimentally reasonable for tiger detection in the wild. This proposed method outperforms previous Amur tiger detection methods presented at CVWC2019.