This project uses deep machine learning to perform object recognition at large scale.
We will be using state of the art deep machine learning tools and technologies to perform object recognition on more than 2,00,000 images. The dataset used here is from imagenet and Microsoft COCO challenge 2015.
http://caffe.berkeleyvision.org/install_apt.html
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev
sudo dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install libatlas-base-dev
To have the Python headers for building the pycaffe interface
sudo apt-get install python-dev
LINUX
cd <installpath>
export LD_LIBRARY_PATH=`pwd`:$LD_LIBRARY_PATH
Add <installpath> to your build and link process by adding -I<installpath> to your compile
line and -L<installpath> -lcudnn to your link line.
sudo cp include/cudnn.h /usr/local/cuda-7.5/include/
sudo cp lib64/libcudnn* /usr/local/cuda-7.5/lib64/
export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
tools/train_net.py --gpu 0 --solver ./models/solvers/solver.prototxt --weights data/imagenet_models/VGG16.v2.caffemodel --imdb coco_2014_train --iters 500 --cfg ./experiments/cfgs/faster_rcnn_end2end.yml
./tools/test_net.py --gpu 0 --def ./models/coco/solvers/test.prototxt --net ./models/bigdata_coco_final.caffemodel --imdb coco_2015_test --cfg experiments/cfgs/faster_rcnn_end2end.yml