Caffe中的标签作为图像 [英] Labels in Caffe as Images

问题描述

我是Caffe的新手.我正在尝试为语义分割实现完全卷积神经网络( FCN-8s ).我有图像数据和标签数据，它们都是图像.这是针对逐像素的预测.

I'm new to Caffe. I am trying to implement a Fully Convolution Neural Network (FCN-8s) for semantic segmentation. I have image data and label data, which are both images. This is for pixel-wise predictions.

我尝试使用ImageData作为数据类型，但它要求输入整数标签，该标签不适用于这种情况.请告知我如何给Caffe一个2D标签.我应该更喜欢LMDB而不是ImageData吗?如果是这样，我该如何进行?对于这种情况，我找不到任何好的教程/文档.

I tried using ImageData as the data type, but it asks for an integer label, which is not applicable to this scenario. Kindly advise as how to I can give Caffe a 2D label. Should I prefer LMDB instead of ImageData? If so, how do I proceed? I could not find any good tutorial/documentation for a situation like this.

推荐答案

由于您需要实现逐像素预测，因此不能将单个标签用作事实.相反，您应该使用标签的真实矩阵.

Since you need to achieve pixel-wise predictions, you can't use a single label as ground-truth. Instead, you should use a ground-truth matrix of labels.

其中一位Caffe专家编写了一个代码片段，用于使用图像数据创建LMDB，请参见

One of the Caffe guys wrote a code snippet for creating an LMDB with image data, see here:

import caffe
import lmdb
from PIL import Image

in_db = lmdb.open('image-lmdb', map_size=int(1e12))
with in_db.begin(write=True) as in_txn:
    for in_idx, in_ in enumerate(inputs):
        # load image:
        # - as np.uint8 {0, ..., 255}
        # - in BGR (switch from RGB)
        # - in Channel x Height x Width order (switch from H x W x C)
        im = np.array(Image.open(in_)) # or load whatever ndarray you need
        im = im[:,:,::-1]
        im = im.transpose((2,0,1))
        im_dat = caffe.io.array_to_datum(im)
        in_txn.put('{:0>10d}'.format(in_idx), im_dat.SerializeToString())
in_db.close()

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