You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Next we compare with standard “Faster R-CNN + ResNet-101” [9] which is the strongest competitor and the top-performer on the PASCAL VOC, MS COCO, and ImageNet benchmarks. We use k\times k=7\times 7 in the following.Table 5 shows the comparisons. Faster R-CNN evaluates a 10-layer subnetwork for each region to achieve good accuracy, but R-FCN has negligible per-region cost.With 300 RoIs at test time, Faster R-CNN takes 0.42s per image, 2.5\times slower than our R-FCN that takes 0.17s per image (on a K40 GPU; this number is 0.11s on a Titan X GPU). R-FCN also trains faster than Faster R-CNN.Moreover, hard example mining [22] adds no cost to R-FCN training (Table 5).It is feasible to train R-FCN when mining from 2000 RoIs, in which case Faster R-CNN is 6\times slower (2.9s vs. 0.46s). But experiments show that mining from a larger set of candidates (e.g., 2000) has no benefit (Table 5). So we use 300 RoIs for both training and inference in other parts of this paper.

Question: What are the evidences that region-wise computations are of low cost ?
As seen in Table 5, Faster R-CNN is 2.5 times slower than R-FCN when mining for 300 RoIs, and 6 times slower when mining for 2000 RoIs, proving that region-wise computations are of low cost.