根据pascal VOC 2007的训练数据集基本架构,第一步,当然是要准备自己的训练图片集,本文直接将自己的准备的图片集(.jpg)扔到如下文件夹下:
$(py-faster-rcnn)/data/VOCdevkit2007/VOC2007/JPEGImages
第二步,根据上述自己的要训练检测的物体图片集,标注相应的.xml文件(我是自己写了一个简单的矩形框标注工具,生成相应的xml文件,在网上找了很久也没找到相应的标注工具,后来只能自己写了),同样与VOC 2007的数据集中的xml文件放在一起,文件夹路径如下:
$(py-faster-rcnn)/data/VOCdevkit2007/VOC2007/Annotations
上述两个pt文件,所要更改的地方基本一样,均是更改num_output的值,由于原来是21类物体检测,本文加入了自己的一类物体进行训练,故由原来的21变成22即可,下面一层相应的变为88。
name: "ZF"
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': 22"
}
}
......
layer {
name: "cls_score"
type: "InnerProduct"
bottom: "fc7"
top: "cls_score"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 22
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: 1.0 }
param { lr_mult: 2.0 }
inner_product_param {
num_output: 88
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0
}
}
}
在下面代码中×××处添加自己加入的类即可。
def __init__(self, image_set, year, devkit_path=None):
datasets.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','×××')
参考资料:
https://github.com/rbgirshick/py-faster-rcnn/issues/34
Process Process-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run
self._target(self._args, *self._kwargs)
File "./tools/train_faster_rcnn_alt_opt.py", line 123, in train_rpn
roidb, imdb = get_roidb(imdb_name)
File "./tools/train_faster_rcnn_alt_opt.py", line 68, in get_roidb
roidb = get_training_roidb(imdb)
File "/home/microway/test/pytest/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 121, in get_training_roidb
imdb.append_flipped_images()
File "/home/microway/test/pytest/py-faster-rcnn/tools/../lib/datasets/imdb.py", line 108, in append_flipped_images
assert (boxes[:, 2] >= boxes[:, 0]).all()
AssertionError
将py-faster-rcnn/lib/datasets/imdb.py中的相应代码改成如下代码即可:
def append_flipped_images(self):
num_images = self.num_images
widths = [PIL.Image.open(self.image_path_at(i)).size[0]
for i in xrange(num_images)]
for i in xrange(num_images):
boxes = self.roidb[i]['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = widths[i] - oldx2 - 1
boxes[:, 2] = widths[i] - oldx1 - 1
for b in range(len(boxes)):
if boxes[b][2] < boxes[b][0]:
boxes[b][0] = 0
assert (boxes[:, 2] >= boxes[:, 0]).all()
File "./tools/train_net.py", line 85, in
roidb = get_training_roidb(imdb)
File "/usr/local/fast-rcnn/tools/../lib/fast_rcnn/train.py", line 111, in get_training_roidb
rdl_roidb.prepare_roidb(imdb)
File "/usr/local/fast-rcnn/tools/../lib/roi_data_layer/roidb.py", line 23, in prepare_roidb
roidb[i]['image'] = imdb.image_path_at(i)
IndexError: list index out of range
删除fast-rcnn-master/data/cache/ 文件夹下的.pkl文件,或者改名备份,重新训练即可。
参考资料:
https://github.com/rbgirshick/py-faster-rcnn/issues/34
https://github.com/rbgirshick/fast-rcnn/issues/79