FANDOM


tensorflow preferred Edit

https://www.youtube.com/watch?v=MDP9FfsNx60 Tensorflow is preferred neural net.

notes Edit

See http://caffe.berkeleyvision.org/ and its github https://github.com/BVLC/caffe Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU* Object recognition with caffe connected to webcam . That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convolution net implementation available.

upload images Edit

http://demo.caffe.berkeleyvision.org/classify_upload classifies images you upload. Take snapshots from cctv stream on Infrared leds trigger classify as human, dog, cat ,car etc. It classifies animals very well, but for some reason it refuses to classify men and women, giving instead a generic "clothing" to "sports equipment". It can identify lipstick and spectacles on a women, but not the women seemingly.

embedded vision Edit

  • https://www.youtube.com/watch?v=XpRqqBmWHXk , http://www.embedded-vision.com/ Intel Graphics is pervasive on client platforms, and when well utilized, it can boost performance of a CNN-based application by factor of 5x compared with a default Caffe CPU + MKL implementation on the same device ("Cherry Trail" Atom x7-z8700, for instance). The demonstrated application is built on top of clCaffe (the Caffe framework extended with OpenCL), which is open sourced for easy reproducibility; its OpenCL code path is performance-optimized for Intel Graphics https://github.com/01org/caffe/tree/clcaffe

OpenKAI Edit

Jetsonhacks Edit

http://www.jetsonhacks.com/2017/03/02/daniel-tobias-car-cherry-autonomous-racecar/ and github cherry autonomous car

Intel github repo Edit

https://github.com/01org , https://github.com/01org/caffe/tree/clcaffe caffe fork

links Edit

Jabelone , EmbeddedPc, GPU

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