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: The Region-based Convolutional Network method (R-CNN) [9] achieves excellent object detection accuracy by using a deep ConvNet to classify object proposals.R-CNN, however, has notable drawbacks:1.Training is a multi-stage pipeline.R-CNN first fine-tunes a ConvNet on object proposals using log loss.Then, it fits SVMs to ConvNet features.These SVMs act as object detectors, replacing the softmax classifier learnt by fine-tuning.In the third training stage, bounding-box regressors are learned.2.Training is expensive in space and time.For SVM and bounding-box regressor training, features are extracted from each object proposal in each image and written to disk.With very deep networks, such as VGG16, this process takes 2.5 GPU-days for the 5k images of the VOC07 trainval set.These features require hundreds of gigabytes of storage.3.Object detection is slow.At test-time, features are extracted from each object proposal in each test image.Detection with VGG16 takes 47s / image (on a GPU). R-CNN is slow because it performs a ConvNet forward pass for each object proposal, without sharing computation.Spatial pyramid pooling networks (SPPnets) [11] were proposed to speed up R-CNN by sharing computation.The SPPnet method computes a convolutional feature map for the entire input image and then classifies each object proposal using a feature vector extracted from the shared feature map.Features are extracted for a proposal by max-pooling the portion of the feature map inside the proposal into a fixed-size output (e.g., 6\times 6).Multiple output sizes are pooled and then concatenated as in spatial pyramid pooling [15].SPPnet accelerates R-CNN by 10 to 100\times at test time.Training time is also reduced by 3\times due to faster proposal feature extraction.

Question: What was the problem of previous work, R-CNN in terms of the efficiency?
R-CNN needs multi-stage pipeline training and time consumes when evaluate them.