update

README.md

@@ -5,7 +5,7 @@ This is the unoffical version  Feature Pyramid Network for Feature Pyramid Netwo
 
 the mxnet unoffical version  Feature Pyramid Network: https://github.com/unsky/Feature-Pyramid-Networks 
 # usage
-dowload VGG16 `VGG16.v2.caffemodel` and your dataset
+dowload  `ResNet50.caffemodel` and your dataset
 ### make caffe
 ```
 cd FP-caffe
@@ -22,9 +22,9 @@ make
 ```
 ### train
 
-./experiments/scripts/FP_Net_end2end.sh 1 VGG16 pascal_voc
+./experiments/scripts/FP_Net_end2end.sh 1 FPN pascal_voc
 
 ### test
-./test.sh 1 VGG16 pascal_voc
+./test.sh 1 FPN pascal_voc
 
 if you have issue about the fpn, open an issue.

caffe-FP_Net/Makefile.config

@@ -0,0 +1,112 @@
+## Refer to http://caffe.berkeleyvision.org/installation.html
+# Contributions simplifying and improving our build system are welcome!
+
+# cuDNN acceleration switch (uncomment to build with cuDNN).
+USE_CUDNN := 1
+
+# CPU-only switch (uncomment to build without GPU support).
+# CPU_ONLY := 1
+
+# uncomment to disable IO dependencies and corresponding data layers
+# USE_OPENCV := 0
+# USE_LEVELDB := 0
+# USE_LMDB := 0
+
+# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
+#       You should not set this flag if you will be reading LMDBs with any
+#       possibility of simultaneous read and write
+# ALLOW_LMDB_NOLOCK := 1
+
+# Uncomment if you're using OpenCV 3
+OPENCV_VERSION := 3
+
+# To customize your choice of compiler, uncomment and set the following.
+# N.B. the default for Linux is g++ and the default for OSX is clang++
+# CUSTOM_CXX := g++
+
+# CUDA directory contains bin/ and lib/ directories that we need.
+CUDA_DIR := /usr/local/cuda
+# On Ubuntu 14.04, if cuda tools are installed via
+# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
+# CUDA_DIR := /usr
+
+# CUDA architecture setting: going with all of them.
+# For CUDA < 6.0, comment the *_50 lines for compatibility.
+CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
+                -gencode arch=compute_20,code=sm_21 \
+                -gencode arch=compute_30,code=sm_30 \
+                -gencode arch=compute_35,code=sm_35 \
+                -gencode arch=compute_50,code=sm_50 \
+                -gencode arch=compute_50,code=compute_50
+
+# BLAS choice:
+# atlas for ATLAS (default)
+# mkl for MKL
+# open for OpenBlas
+BLAS := atlas
+# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
+# Leave commented to accept the defaults for your choice of BLAS
+# (which should work)!
+# BLAS_INCLUDE := /path/to/your/blas
+# BLAS_LIB := /path/to/your/blas
+
+# Homebrew puts openblas in a directory that is not on the standard search path
+# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
+# BLAS_LIB := $(shell brew --prefix openblas)/lib
+
+# This is required only if you will compile the matlab interface.
+# MATLAB directory should contain the mex binary in /bin.
+# MATLAB_DIR := /usr/local
+# MATLAB_DIR := /Applications/MATLAB_R2012b.app
+
+# NOTE: this is required only if you will compile the python interface.
+# We need to be able to find Python.h and numpy/arrayobject.h.
+#PYTHON_INCLUDE := /usr/include/python2.7 \
+                /usr/lib/python2.7/dist-packages/numpy/core/include
+# Anaconda Python distribution is quite popular. Include path:
+# Verify anaconda location, sometimes it's in root.
+ANACONDA_HOME := /usr/local/anaconda2
+PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
+                $(ANACONDA_HOME)/include/python2.7 \
+                $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
+
+# Uncomment to use Python 3 (default is Python 2)
+# PYTHON_LIBRARIES := boost_python3 python3.5m
+# PYTHON_INCLUDE := /usr/include/python3.5m \
+#                 /usr/lib/python3.5/dist-packages/numpy/core/include
+
+# We need to be able to find libpythonX.X.so or .dylib.
+# PYTHON_LIB := /usr/lib
+PYTHON_LIB := $(ANACONDA_HOME)/lib
+
+# Homebrew installs numpy in a non standard path (keg only)
+# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
+# PYTHON_LIB += $(shell brew --prefix numpy)/lib
+
+# Uncomment to support layers written in Python (will link against Python libs)
+WITH_PYTHON_LAYER := 1
+
+# Whatever else you find you need goes here.
+INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
+LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
+
+# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
+# INCLUDE_DIRS += $(shell brew --prefix)/include
+# LIBRARY_DIRS += $(shell brew --prefix)/lib
+
+# Uncomment to use `pkg-config` to specify OpenCV library paths.
+# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
+# USE_PKG_CONFIG := 1
+
+# N.B. both build and distribute dirs are cleared on `make clean`
+BUILD_DIR := build
+DISTRIBUTE_DIR := distribute
+
+# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
+# DEBUG := 1
+
+# The ID of the GPU that 'make runtest' will use to run unit tests.
+TEST_GPUID := 0
+
+# enable pretty build (comment to see full commands)
+Q ?= @

caffe-FP_Net/README.md

@@ -1,37 +1 @@
-# Caffe
-
-[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
-[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
-
-Caffe is a deep learning framework made with expression, speed, and modularity in mind.
-It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
-
-Check out the [project site](http://caffe.berkeleyvision.org) for all the details like
-
-- [DIY Deep Learning for Vision with Caffe](https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.p)
-- [Tutorial Documentation](http://caffe.berkeleyvision.org/tutorial/)
-- [BVLC reference models](http://caffe.berkeleyvision.org/model_zoo.html) and the [community model zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
-- [Installation instructions](http://caffe.berkeleyvision.org/installation.html)
-
-and step-by-step examples.
-
-[![Join the chat at https://gitter.im/BVLC/caffe](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/BVLC/caffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-
-Please join the [caffe-users group](https://groups.google.com/forum/#!forum/caffe-users) or [gitter chat](https://gitter.im/BVLC/caffe) to ask questions and talk about methods and models.
-Framework development discussions and thorough bug reports are collected on [Issues](https://github.com/BVLC/caffe/issues).
-
-Happy brewing!
-
-## License and Citation
-
-Caffe is released under the [BSD 2-Clause license](https://github.com/BVLC/caffe/blob/master/LICENSE).
-The BVLC reference models are released for unrestricted use.
-
-Please cite Caffe in your publications if it helps your research:
-
-    @article{jia2014caffe,
-      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
-      Journal = {arXiv preprint arXiv:1408.5093},
-      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
-      Year = {2014}
-    }
+deformable convolution net on caffe