ZF网络已经训练通过，参考训练ZF的步骤修改相关文件。环境：CPU+ Ubuntu16.04
1、修改py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt & stage2_fast_rcnn_train.pt
layer { name: 'data' type: 'Python' top: 'data' top: 'rois' top: 'labels' top: 'bbox_targets' top: 'bbox_inside_weights' top: 'bbox_outside_weights' python_param { module: 'roi_data_layer.layer' layer: 'RoIDataLayer' param_str: "'num_classes': 2"#original is 21 ,class_num + 1 } } layer { name: "cls_score" type: "InnerProduct" bottom: "fc7" top: "cls_score" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 2 # origial is 21, class_num + 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "bbox_pred" type: "InnerProduct" bottom: "fc7" top: "bbox_pred" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 8 # original is 84, (class_num+1)*4 weight_filler { type: "gaussian" std: 0.001 } bias_filler { type: "constant" value: 0 } } }2、修改py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/stage1_rpn_train.pt & stage2_rpn_train.pt layer { name: 'input-data' type: 'Python' top: 'data' top: 'im_info' top: 'gt_boxes' python_param { module: 'roi_data_layer.layer' layer: 'RoIDataLayer' param_str: "'num_classes': 2" # original is 21, class_num + 1 } }3、py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/faster_rcnn_test.pt layer { name: "cls_score" type: "InnerProduct" bottom: "fc7" top: "cls_score" inner_product_param { num_output: 2 # original is 21, class_num + 1 } } layer { name: "bbox_pred" type: "InnerProduct" bottom: "fc7" top: "bbox_pred" inner_product_param { num_output: 8 # original is 84, (class_num + 1)*4 } }4、py-faster-rcnn/lib/datasets/pascal_voc.py修改（ZF时已修改） def __init__(self, image_set, year, devkit_path=None): imdb.__init__(self, 'voc_' + year + '_' + image_set) self._year = year self._image_set = image_set self._devkit_path = self._get_default_path() if devkit_path is None \ else devkit_path self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year) self._classes = ('__background__', # always index 0 #'aeroplane', 'bicycle', 'bird', 'boat', #'bottle', 'bus', 'car', 'cat', 'chair', #'cow', 'diningtable', 'dog', 'horse', #'motorbike', 'person', 'pottedplant', #'sheep', 'sofa', 'train', 'tvmonitor' 'leftAtrial' )5、py-faster-rcnn/lib/datasets/imdb.py修改（ZF时已修改） 将append_flipped_images(self)函数的第二行widths = self._get_widths()改为如下形式： widths = [PIL.Image.open(self.image_path_at(i)).size[0] for i in xrange(num_images)]6、修改tools/train_faster_rcnn_alt_opt.py中的迭代次数
max_iters = [8000, 4000, 8000, 4000] 修改models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/下对应的solver文件的stepsize（学习率等超参也在这里修改），要小于max_iters stepsize: 3000 # original is 30000, should smaller than max_iters[1]#stage1_fast_rcnn_solver30k40k.pt stepsize: 6000 # original is 60000, should smaller than max_iters[0]#stage1_rpn_solver60k80k.pt stepsize: 3000 # original is 30000, should smaller than max_iters[3]#stage2_fast_rcnn_solver30k40k.pt stepsize: 6000 # original is 60000, should smaller than max_iters[2]#stage2_rpn_solver60k80k.pt将solver中第一行的路径改为绝对路径，否则会报找不到路径的错误
train_net: "/home/lys/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt" 7、删除output以及/data/cache，py-faster-rcnn/data/VOCdevkit2007/annotation_cache（使用test_net.py测试时产生）将对应的文件放入/data/VOCdevkit2007/VOC2007/8、训练：
python train_faster_rcnn_alt_opt.py --net_name VGG_CNN_M_1024 --weights /home/lys/py-faster-rcnn/data/imagenet_models/VGG_CNN_M_1024.v2.caffemodel --cfg /home/lys/py-faster-rcnn/experiments/cfgs/faster_rcnn_alt_opt.yml --imdb voc_2007_trainval I0831 11:23:16.666177 22761 solver.cpp:229] Iteration 0, loss = 1.16274 I0831 11:23:16.666204 22761 solver.cpp:245] Train net output #0: rpn_cls_loss = 0.689726 (* 1 = 0.689726 loss) I0831 11:23:16.666209 22761 solver.cpp:245] Train net output #1: rpn_loss_bbox = 0.473018 (* 1 = 0.473018 loss) I0831 11:23:16.666214 22761 sgd_solver.cpp:106] Iteration 0, lr = 0.001 I0831 11:24:29.267117 22761 solver.cpp:229] Iteration 20, loss = 0.560839 I0831 11:24:29.267145 22761 solver.cpp:245] Train net output #0: rpn_cls_loss = 0.495616 (* 1 = 0.495616 loss) I0831 11:24:29.267151 22761 solver.cpp:245] Train net output #1: rpn_loss_bbox = 0.0652227 (* 1 = 0.0652227 loss)9、使用demo.py进行测试
需要加入NETS中加入'vgg_m':('VGG_CNN_M_1024', 'VGG_CNN_M_1024_faster_rcnn_final.caffemodel')，红色部分应该与/models/pascal_voc/下的VGG_CNN_M_1024文件名相同。
python demo.py --net vgg_m --cpu