自己根据AlexNet和FCN做的PPT，讲述了卷积神经网络的大致流程和工作原理，博主鄙见，与君共享，还望指正！

一键接入,支持window版,Linux,下载解压就可以使用,里面有详细的说明书,按照说明书操作即可

此文件描述了FCN 代码工程的运行步骤，讲述了所需要的配置环境，和每一步运行后的结果，最终给出了训练模型结束后的分割结果。

参考：https://blog.csdn.net/Sousky/article/details/88298059

tensorflow实现FCN_源代码，可以在自己的电脑上跑程序

FCN的必备文件MITSceneParsing.pickle，折腾了半天终于get,在此发出来与大家分享！

First release: 26 October 2017 www.sciencemag.org (Page numbers not final at time of first release) 1 The ability to learn and generalize from a few examples is a hallmark of human intelligence (1). CAPTCHAs, images used by websites to block automated interactions, are examples of problems that are easy for humans but difficult for comput-ers. CAPTCHAs are hard for algorithms because they add clutter and crowd letters together to create a chicken-and-egg problem for character classifiers — the classifiers work well for characters that have been segmented out, but segmenting the individual characters requires an understanding of the characters, each of which might be rendered in a combinato-rial number of ways (2–5). A recent deep-learning approach for parsing one specific CAPTCHA style required millions of labeled examples from it (6), and earlier approaches mostly relied on hand-crafted style-specific heuristics to segment out the character (3, 7); whereas humans can solve new styles without explicit training (Fig. 1A). The wide variety of ways in which letterforms could be rendered and still be under-stood by people is illustrated in Fig. 1. Building models that generalize well beyond their train-ing distribution is an important step toward the flexibility Douglas Hofstadter envisioned when he said that “for any program to handle letterforms with the flexibility that human beings do, it would have to possess full-scale artificial intelli-gence” (8). Many researchers have conjectured that this could be achieved by incorporating the inductive biases of the vis-ual cortex (9–12), utilizing the wealth of data generated by neuroscience and cognitive science research. In the mamma-lian brain, feedback connections in the visual cortex play roles in figure-ground-segmentation, and in object-based top-down attention that isolates the contours of an object even when partially transparent objects occupy the same spatial locations (13–16). Lateral connections in the visual co

https://github.com/MarvinTeichmann/KittiSeg 运行demo.py时 需要下载KittiSeg_pretrained.zip，但是这个网站"ftp://mi.eng.cam.ac.uk/pub/mttt2/models/KittiSeg_pretrained.zip"总是抽风，所以上传了百度云盘一份

就是两个预训练模型，分别是ResNet-50的和ResNet-101的预训练模型。直接下载解压就行了。对了，是原版的RFCN哦，就是Caffe+Python的，不是tensorflow的model。

图像语义分割 FCN方法，使用tensorflow库，解压使用即可