High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption.
完整的caff的模型包。用法示例代码：
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
#%matplotlib inline
# set display defaults
plt.rcParams['figure.figsize'] = (10, 10) # large images
plt.rcParams['image.interpolat
为了解决在遥感图像场景分类问题中传统的底层或中间级视觉特征无法充分描述复杂场景的问题，提出了采用第三种感知网络（Inspection-v3）、快速特征嵌入的卷积神经网络（CaffeNet）、OverFeatL 3种深度卷积神经网络（DCNN）提取的融合特征进行遥感图像场景分类方法。通过利用利用3种DCNN提取的归一化的融合特征进行分类实验，在UCMLU（University of California Merced Land Use） 数据集上获得了97.01%的准确率。融合特征的分类实验证明，
