ml-lab / py-r-fcn

R-FCN with joint training and python support

py-R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

py-R-FCN now supports joint training.

Disclaimer

The official R-FCN code (written in MATLAB) is available here. If your goal is to reproduce the results in the NIPS 2016 paper, please use the official code.

py-R-FCN is based on the py-faster-rcnn code(include this README) and the offcial R-FCN implementation, and the usage is quite similar to py-faster-rcnn, thanks for their great works.

There are slight differences between the two implementations. In particular, this Python port

• is ~10% slower at test-time, because some operations execute on the CPU in Python layers (e.g., 90ms / image vs. 99ms / image for ResNet-50)
• gives slightly lower mAP as the MATLAB version(The paper mentioned that joint training with OHEM may delivers a lower accuracy(check the footnote at page 4)).

Some modification

The original py-faster-rcnn uses class-aware bounding box regression. However, R-FCN use class-agonistic bounding box regression to reduce model complexity. So I add a configuration AGONISTIC into fast_rcnn/config.py, and the default value is False. You should set it to True both on train and test phase if you want to use class agonistic training and test.

OHEM need all rois to select the hard examples, so I changed the sample strategy, set BATCH_SIZE: -1 for OHEM, otherwise OHEM would not take effect.

In conclusion:

AGONISTIC: True is required for class-agonistic bounding box regression

BATCH_SIZE: -1 is required for OHEM

And I've already provided two configuration files for you(w/ OHEM and w/o OHEM) under experiments/cfgs folder, you could just use them and needn't change anything.

License

R-FCN is released under the MIT License (refer to the LICENSE file for details).

Citing R-FCN

If you find R-FCN useful in your research, please consider citing:

@article{dai16rfcn,
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Journal = {arXiv preprint arXiv:1605.06409},
    Year = {2016}
}

Main Results

               | training data       | test data             | mAP   | time/img (K40) | time/img (Titian X)

-------------------|:-------------------:|:---------------------:|:-----:|:--------------:|:------------------:| R-FCN, ResNet-50 | VOC 07+12 trainval | VOC 07 test | 76.9%(80k110k) | - | 0.099sec | R-FCN, ResNet-101 | VOC 07+12 trainval | VOC 07 test | 78.7%(80k110k) | - | 0.136sec |

Requirements: software

0. Important Please use the Microsoft-version Caffe(@commit 1a2be8e), this Caffe supports R-FCN layer, and the prototxt in this repository follows the Microsoft-version Caffe's layer name. You need to put the Caffe root folder under py-R-FCN folder, just like what py-faster-rcnn does.

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)

Note: Caffe must be built with support for Python layers!

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# Unrelatedly, it's also recommended that you use CUDNN
USE_CUDNN := 1

2. Python packages you might not have: cython, python-opencv, easydict
3. [Optional] MATLAB is required for official PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.

Requirements: hardware

Any NVIDIA GPU with 6GB or larger memory is OK(4GB is enough for ResNet-50).

Installation

1. Clone the R-FCN repository

git clone https://github.com/Orpine/py-R-FCN.git

We'll call the directory that you cloned R-FCN into RFCN_ROOT

2. Clone the Caffe repository

cd $RFCN_ROOT
git clone https://github.com/Microsoft/caffe.git

[optional]

cd caffe
git reset --hard 1a2be8e

(I only test on this commit, and I'm not sure whether this Caffe is still compatible with the prototxt in this repository in the future)

If you followed the above instruction, python code will add $RFCN_ROOT/caffe/python to PYTHONPATH automatically, otherwise you need to add $CAFFE_ROOT/python by your own, you could check $RFCN_ROOT/tools/_init_paths.py for more details.

3. Build the Cython modules

   cd $RFCN_ROOT/lib
   make

4. Build Caffe and pycaffe

   cd $RFCN_ROOT/caffe
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make -j8 && make pycaffe

Demo

1. To use demo you need to download the pretrained R-FCN model, please download the model manually from OneDrive, and put it under $RFCN/data.

   Make sure it looks like this:

   $RFCN/data/rfcn_models/resnet50_rfcn_final.caffemodel
   $RFCN/data/rfcn_models/resnet101_rfcn_final.caffemodel

2. To run the demo

   $RFCN/tools/demo_rfcn.py

The demo performs detection using a ResNet-101 network trained for detection on PASCAL VOC 2007.

Preparation for Training & Testing

1. Download the training, validation, test data and VOCdevkit

   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

2. Extract all of these tars into one directory named VOCdevkit

   tar xvf VOCtrainval_06-Nov-2007.tar
   tar xvf VOCtest_06-Nov-2007.tar
   tar xvf VOCdevkit_08-Jun-2007.tar
   tar xvf VOCtrainval_11-May-2012.tar

3. It should have this basic structure

   $VOCdevkit/                           # development kit
   $VOCdevkit/VOCcode/                   # VOC utility code
   $VOCdevkit/VOC2007                    # image sets, annotations, etc.
   $VOCdevkit/VOC2012                    # image sets, annotations, etc.
   # ... and several other directories ...

4. Since py-faster-rcnn does not support multiple training datasets, we need to merge VOC 2007 data and VOC 2012 data manually. Just make a new directory named VOC0712, put all subfolders except ImageSets in VOC2007 and VOC2012 into VOC0712(you'll merge some folders). I provide a merged-version ImageSets folder for you, please put it into VOCdevkit/VOC0712/.

5. Then the folder structure should look like this

	$VOCdevkit/                           # development kit
	$VOCdevkit/VOCcode/                   # VOC utility code
	$VOCdevkit/VOC2007                    # image sets, annotations, etc.
	$VOCdevkit/VOC2012                    # image sets, annotations, etc.
	$VOCdevkit/VOC0712                    # you just created this folder
	# ... and several other directories ...

4. Create symlinks for the PASCAL VOC dataset

   cd $RFCN_ROOT/data
   ln -s $VOCdevkit VOCdevkit0712

5. Please download ImageNet-pre-trained ResNet-50 and ResNet-100 model manually, and put them into $RFCN_ROOT/data/imagenet_models

6. Then everything is done, you could train your own model.

Usage

To train and test a R-FCN detector using the approximate joint training method, use experiments/scripts/rfcn_end2end.sh. Output is written underneath $RFCN_ROOT/output.

To train and test a R-FCN detector using the approximate joint training method with OHEM, use experiments/scripts/rfcn_end2end_ohem.sh. Output is written underneath $RFCN_ROOT/output.

cd $RFCN_ROOT
./experiments/scripts/rfcn_end2end[_ohem].sh [GPU_ID] [NET] [DATASET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {ResNet-50, ResNet-101} is the network arch to use
# DATASET in {pascal_voc, coco} is the dataset to use(I only tested on pascal_voc)
# --set ... allows you to specify fast_rcnn.config options, e.g.
#   --set EXP_DIR seed_rng1701 RNG_SEED 1701

Trained R-FCN networks are saved under:

output/<experiment directory>/<dataset name>/

Test outputs are saved under:

output/<experiment directory>/<dataset name>/<network snapshot name>/

Misc

Tested on Ubuntu 14.04 with a Titan X GPU and Intel Xeon CPU E5-2620 v2 @ 2.10GHz

py-faster-rcnn code can also work properly, but I do not add any other feature(such as ResNet and OHEM).