semantic segmentation mobile

This is CVPR 2015 to get the best paper candidate paper.Paper: Fully convolutional Networks for Semantic segmentationRespect the original, reproduced please specify: http://blog.csdn.net/tangwei20141. Overview Key contributionsThis paper presents a end-to-end method of semantic segmentation, referred to as FCN.As shown, directly take segmentation's ground truth

$ and $ f_7 $, on the contrary, the extracted features of $ f_6 $ are slightly higher than those of $ f_7 $. We can sum up a rule that uses CNN as the Feature Extraction Tool instead of fine-tuning specific tasks. The features learned by the convolution layer are actually basic generalized features, in the future, the full connection layer will learn more about the representation of specific tasks. Question 2: Why does CNN output support classification through a softmax layer? Through the above

1. Weak supervisionAs the company recently prepared to open a new project, using deep learning to train a model that can be automatically labeled, but the model required training set is more troublesome, to first use ffmpeg from the video to intercept a video, in the use of OPENCV to draw frames to get pictures, so I can only be the semantic segmentation of the JSON file and the original image, The mask of

sys.path.append (' Caffe root directory/python ')Another way to do this once and for all is to add the interface to the Pythonpath in the terminal or BASHRC: Export Pythonpath=caffe root directory/python: $PYTHONPATH This time we use the latter. A file was found in the root directory of the decompression code: infer.py. Slightly modified infer.py, you can test our own pictures, please according to their actual situation to make changes. im = Image.open (' voc-fcn8s/test.jpeg ') here refers to