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: In this paper, we develop a framework called Region-based Fully Convolutional Network (R-FCN) for object detection. Our network consists of shared, fully convolutional architectures as is the case of FCN [15]. To incorporate translation variance into FCN, we construct a set of position-sensitive score maps by using a bank of specialized convolutional layers as the FCN output. Each of these score maps encodes the position information with respect to a relative spatial position (e.g., “to the left of an object”). On top of this FCN, we append a position-sensitive RoI pooling layer that shepherds information from these score maps, with no weight (convolutional/fc) layers following. The entire architecture is learned end-to-end. All learnable layers are convolutional and shared on the entire image, yet encode spatial information required for object detection. Figure 1 illustrates the key idea and Table 1 compares the methodologies among region-based detectors. The importance of position-sensitivity is further demonstrated by setting k=1, for which R-FCN is unable to converge. In this degraded case, no spatial information can be explicitly captured within an RoI. Moreover, we report that naïve Faster R-CNN is able to converge if its RoI pooling output resolution is 1\times 1, but the mAP further drops by a large margin to 61.7% (Table 2). Given the proposal regions (RoIs), the R-FCN architecture is designed to classify the RoIs into object categories and background. In R-FCN, all learnable weight layers are convolutional and are computed on the entire image. The last convolutional layer produces a bank of k^{2} position-sensitive score maps for each category, and thus has a k^{2}(C+1)-channel output layer with C object categories (+1 for background). The bank of k^{2} score maps correspond to a k\times k spatial grid describing relative positions. For example, with k\times k=3\times 3, the 9 score maps encode the cases of {top-left, top-center, top-right, …, bottom-right} of an object category.

Question: Why was R-FCN unable to converge using only one score map?
R-FCN depends on the score maps of R-FCN for its output as the score maps are used for the rod pooling layer. Therefore, when the k = 1 and there is only one score map, RoIs don't actually capture any spatial information and therefore the model fails to learn the task.