• Require latest MXNet. Set environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT=0.
• Install Python package mxnet or mxnet-cu90, cython and opencv-python matplotlib pycocotools tqdm.

Download any of the following models and run python3 demo.py --params $MODEL_FILE$ --network $Network$ --dataset $Dataset$ to get single image inference. Different dataset has different object class names.

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.

• 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.

Use python3 train.py --pretrained $IMAGENET_MODEL_FILE$ --network $Network$ --dataset $Dataset$ to train, python3 test.py --params $MODEL_FILE$ --network $Network --dataset $Dataset$ to evaluate.

• May 25, 2016: We released Fast R-CNN implementation.
• July 6, 2016: We released Faster R-CNN implementation.
• July 23, 2016: We updated to MXNet module solver.
• Oct 10, 2016: tornadomeet released approximate end-to-end training.
• Oct 30, 2016: We updated to MXNet module inference.
• Jan 19, 2017: We accelerated our pipeline and supported ResNet training.
• Jun 22, 2018: We simplified code.

This repository used code from MXNet, Fast R-CNN, Faster R-CNN, caffe, tornadomeet/mx-rcnn, MS COCO API.
Thanks to tornadomeet for end-to-end experiments and MXNet contributers for helpful discussions.

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