This project was forked from: https://github.com/endernewton/tf-faster-rcnn. Therefore, first of all thanks.

This project just use the test mode adding augmentations to the original image.

• A basic Tensorflow installation. The code follows r1.2 format. If you are using r1.0, please check out the r1.0 branch to fix the slim Resnet block issue. If you are using an older version (r0.1-r0.12), please check out the r0.12 branch. While it is not required, for experimenting the original RoI pooling (which requires modification of the C++ code in tensorflow), you can check out my tensorflow fork and look for tf.image.roi_pooling.
• Python packages you might not have: cython, opencv-python, easydict (similar to py-faster-rcnn). For easydict make sure you have the right version. I use 1.6.
• Docker users: Since the recent upgrade, the docker image on docker hub (https://hub.docker.com/r/mbuckler/tf-faster-rcnn-deps/) is no longer valid. However, you can still build your own image by using dockerfile located at docker folder (cuda 8 version, as it is required by Tensorflow r1.0.) And make sure following Tensorflow installation to install and use nvidia-docker[https://github.com/NVIDIA/nvidia-docker]. Last, after launching the container, you have to build the Cython modules within the running container.

1. Clone the repository and darknet submodule

git clone https://github.com/ccrafael/tf-faster-rcnn.git --recursive

2. Update your -arch in setup script to match your GPU

cd tf-faster-rcnn/lib
# Change the GPU architecture (-arch) if necessary
vim setup.py

Note: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs. Also even if you are only using CPU tensorflow, GPU based code (for NMS) will be used by default, so please set USE_GPU_NMS False to get the correct output.

3. Build the Cython modules

make clean
make
cd ..

4. Build the darknet submodule to build YOLOv2

cd darknet
make

Please follow the instructions of py-faster-rcnn here to setup VOC and COCO datasets (Part of COCO is done). The steps involve downloading data and optionally creating soft links in the data folder. Since faster RCNN does not rely on pre-computed proposals, it is safe to ignore the steps that setup proposals.

If you find it useful, the data/cache folder created on my side is also shared here.

Download the models through the link:

• Another server here.
• Google drive here.

2. Create a folder and a soft link to use the pre-trained model

NET=res101
TRAIN_IMDB=voc_2007_trainval+voc_2012_trainval
mkdir -p output/${NET}/${TRAIN_IMDB}
cd output/${NET}/${TRAIN_IMDB}
ln -s ../../../data/voc_2007_trainval+voc_2012_trainval ./default
cd ../../..

The command for testing: /usr/bin/python2.7 tools/test_net.py --imdb voc_2007_test --model output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_70000.ckpt --cfg experiments/cfgs/vgg16.yml --tag ${EXTRA_ARGS_SLUG} --net vgg16 --set ANCHOR_SCALES [8,16,32] ANCHOR_RATIOS [0.5,1,2] /home