Parallel Faster R-CNN implementation with MXNet

Faster R-CNN in MXNet

Parallel Faster R-CNN implementation with MXNet.

Set up environment

• Require latest MXNet. Set environment variable by export MXNET_CUDNN_AUTOTUNE_DEFAULT=0.
• Install Python package mxnet (cpu inference only) or mxnet-cu90 (gpu training), cython then opencv-python matplotlib pycocotools tqdm.

Out-of-box inference models

Download any of the following models to the current directory and run python3 demo.py --dataset $Dataset$ --network $Network$ --params $MODEL_FILE$ --image $YOUR_IMAGE$ to get single image inference.
For example python3 demo.py --dataset voc --network vgg16 --params vgg16_voc0712.params --image myimage.jpg, add --gpu 0 to use GPU optionally.
Different network has different configuration. Different dataset has different object class names. You must pass them explicitly as command line arguments.

Download data and label

Make a directory data and follow py-faster-rcnn for data preparation instructions.

• Pascal VOC should be in data/VOCdevkit containing VOC2007, VOC2012 and annotations.
• MSCOCO should be in data/coco containing train2017, val2017 and annotations/instances_train2017.json, annotations/instances_val2017.json.

Download pretrained ImageNet models

• VGG16 should be at model/vgg16-0000.params from MXNet model zoo.
• ResNet should be at model/resnet-101-0000.params from MXNet model zoo.

Training and evaluation

Use python3 train.py --dataset $Dataset$ --network $Network$ --pretrained $IMAGENET_MODEL_FILE$ --gpus $GPUS$ to train,
for example, python3 train.py --dataset voc --network vgg16 --pretrained model/vgg16-0000.params --gpus 0,1.
Use python3 test.py --dataset $Dataset$ --network $Network$ --params $MODEL_FILE$ --gpu $GPU$ to evaluate,
for example, python3 test.py --dataset voc --network vgg16 --params model/vgg16-0010.params --gpu 0.

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