/FasterRCNNbyKeras

This repo is an implementation of faster r-cnn using Keras and Tensorflow. The Tensorflow part borrows the ref: https://github.com/endernewton/tf-faster-rcnn a lot.

Primary LanguagePythonMIT LicenseMIT

keras_frcnn

This repo is an implementation of Faster R-CNN integrating both Keras and Tensorflow. We use a lot of endernewton‘s tensorflow code, and the reference is that: https://github.com/endernewton/tf-faster-rcnn

Introduction

Our implementation is aiming to build the Keras interface based on a Tensorflow Faster R-CNN code [1]. Most of the tensorflow functions are packed by using Keras Lambda function. We reconstruct the code based on the above goals.

Benchmark

  • Pascal VOC 2007
model #GPUs batch size lr max_epoch mem/GPU mAP (%)
VGG-16     1 1   1e-5 7 8817 MB 66.0
  • Pascal VOC 2007 + 2012
model #GPUs batch size lr max_epoch mem/GPU mAP (%)
VGG-16     1 1   1e-5 7 8817 MB 72.2
  • COCO 2014
model #GPUs batch size lr max_epoch mem/GPU mAP (%)
VGG-16     1 1   1e-5 7 8817 MB 31.2

Required Environment

  • Ubuntu 16.04

  • Python 2/3, in case you need the sufficient scientific computing packages, we recommend you to install anaconda.

  • Tensorflow >= 1.5.0

  • Keras >= 2.2.0

  • Optional: if you need GPUs acceleration, please install CUDA that the version requires >= 9.0

Tutorial

  • Data preparation and setup

    Follow the ref: https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models. Download Pascal VOC 07 dataset and build soft link in folder ./data/, then name the link as "VOCdevkit2007"

    To compile the lib, move into the lib folder "cd ./lib". According to your hardware, change the "-arch" parameter in setup.py line: 130. Then run

    make

    Tips: to find your hardware compiling setting, you can refer to: http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/. For me using the Titan XP, I set the "-arch" as "sm_61".

  • Training

    Build the model weight saving folder "../output/[NET]/"

    Download pre-trained models and weights:

    mkdir net_weights
wget https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5
cd ..

    Run the following script:

    ./scripts/train_faster_rcnn.sh [GPU_ID] [DATASET] [NET]
# GPU_ID is the GPU you want to test on
# NET in {vgg16} is the network arch to use,
# DATASET {pascal_voc, pascal_voc_0712, coco} is defined in train_faster_rcnn.sh
# Examples:
./scripts/train_faster_rcnn.sh 0 pascal_voc vgg16
  • Testing

    Build the test output saving folder "../output/[NET]/"

    Run the following script:

    ./scripts/test_faster_rcnn.sh [GPU_ID] [DATASET] [NET]
# GPU_ID is the GPU you want to test on
# NET in {vgg16} is the network arch to use,
# DATASET {pascal_voc, pascal_voc_0712, coco} is defined in train_faster_rcnn.sh
# Examples:
./scripts/test_faster_rcnn.sh 0 pascal_voc vgg16

Reference