Faster R-CNN Implementation with MXNet Gluon API

This repo re-implements Faster R-CNN fully on MXNet Gluon API, which supports batch size larger than one and Multi-GPU training. You can use the code to train/validate/test for object detection task.

Features

•  RPN, Fast R-CNN, Faster R-CNN with VGG16 model

•  Inference and prediction in hybridize mode

•  Multi-GPU and lager batch size support

•  End to end training/validating/testing

•  Alternate training/validating/testing

More functions are in developing...

Requirements

Note: This repo depends on MXNet version 1.2.1+, due to MXNet Symbol and Gluon Proposal API are inconsistent in previous version.

This repo requires Python3 with the following packages:

mxnet
tqdm
EasyDict
matplotlib
opencv-python

You may also need a GPU with at least 8GB memory for training.

Installation

1. Install all required packages in python3.

2. Clone the Gluon Faster R-CNN repository.

   git clone https://github.com/WalterMa/gluon-faster-rcnncd gluon-faster-rcnn

Try the Demo

3. Download pre-trained model parameters from release. Then extract it to ./model directory.

4. Run demo_faster_rcnn.py.

   python ./demo_faster_rcnn.py

Train & Testing Faster R-CNN

Currently, this repo only support voc2007/2012 dataset. But you could easily modify or create your own dataset by reference Gluon-CV dataset code, or generate and using record dataset.

Note: Record Dataset is only available in num_workers=0, due to MXNet issue.

3. We need the following three files from Pascal VOC:

   Filename	Size	SHA-1
   VOCtrainval_06-Nov-2007.tar	439 MB	34ed68851bce2a36e2a223fa52c661d592c66b3c
   VOCtest_06-Nov-2007.tar	430 MB	41a8d6e12baa5ab18ee7f8f8029b9e11805b4ef1
   VOCtrainval_11-May-2012.tar	1.9 GB	4e443f8a2eca6b1dac8a6c57641b67dd40621a49

4. Download and extract voc dataset to ./data/VOCdevkit/, or you need to specify dataset path in .utils/config.py or related python scripts.

5. Start e2e training and validating:

   python ./train_faster_rcnn.py

Experiments

Disclaimer

This is a re-implementation of original Faster R-CNN which is based on caffe. The arXiv paper is available here.

This repository used code from MXNet, Faster R-CNN, MX R-CNN, MXNet SSD, Gluon CV.