Faster R-CNN / Mask R-CNN on COCO

This example provides a minimal (2k lines) and faithful implementation of the following papers:

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Feature Pyramid Networks for Object Detection
• Mask R-CNN
• Cascade R-CNN: Delving into High Quality Object Detection

with the support of:

• Multi-GPU / distributed training
• Cross-GPU BatchNorm
• Group Normalization

Dependencies

• Python 3; OpenCV.
• TensorFlow >= 1.6 (1.4 or 1.5 can run but may crash due to a TF bug);
• pycocotools: pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
• Pre-trained ImageNet ResNet model from tensorpack model zoo.
• COCO data. It needs to have the following directory structure:

COCO/DIR/
  annotations/
    instances_train201?.json
    instances_val201?.json
  train201?/
    COCO_train201?_*.jpg
  val201?/
    COCO_val201?_*.jpg

You can use either the 2014 version or the 2017 version of the dataset. To use the common "trainval35k + minival" split for the 2014 dataset, just download the annotation files instances_minival2014.json, instances_valminusminival2014.json from here to annotations/ as well.

^{Note that train2017==trainval35k==train2014+val2014-minival2014, and val2017==minival2014}

Usage

Train:

On a single machine:

./train.py --config \
    MODE_MASK=True MODE_FPN=True \
    DATA.BASEDIR=/path/to/COCO/DIR \
    BACKBONE.WEIGHTS=/path/to/ImageNet-R50-Pad.npz \

To run distributed training, set TRAINER=horovod and refer to HorovodTrainer docs.

Options can be changed by either the command line or the config.py file. Recommended configurations are listed in the table below.

The code is only valid for training with 1, 2, 4 or >=8 GPUs. Not training with 8 GPUs may result in different performance from the table below.

Inference:

To predict on an image (and show output in a window):

./train.py --predict input.jpg --load /path/to/model --config SAME-AS-TRAINING

To evaluate the performance of a model on COCO:

./train.py --evaluate output.json --load /path/to/COCO-R50C4-MaskRCNN-Standard.npz \
    --config SAME-AS-TRAINING

Several trained models can be downloaded in the table below. Evaluation and prediction will need to be run with the corresponding training configs.

Results

These models are trained on trainval35k and evaluated on minival2014 using mAP@IoU=0.50:0.95. Performance in Detectron can be roughly reproduced. Mask R-CNN results contain both box and mask mAP.

Backbone	mAP (box;mask)	Detectron mAP 1 (box;mask)	Time on 8 V100s	Configurations (click to expand)
R50-C4	33.1		18h	super quick MODE_MASK=False FRCNN.BATCH_PER_IM=64 PREPROC.SHORT_EDGE_SIZE=600 PREPROC.MAX_SIZE=1024 TRAIN.LR_SCHEDULE=[150000,230000,280000]
R50-C4	36.6	36.5	44h	standard MODE_MASK=False
R50-FPN	37.4	37.9	29h	standard MODE_MASK=False MODE_FPN=True
R50-C4	38.2;33.3 ⬇️	37.8;32.8	49h	standard this is the default
R50-FPN	38.5;35.2 ⬇️	38.6;34.5	30h	standard MODE_FPN=True
R50-FPN	42.0;36.3		41h	+Cascade MODE_FPN=True FPN.CASCADE=True
R50-FPN	39.5;35.2	39.5;34.42	33h	+ConvGNHead MODE_FPN=True FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head
R50-FPN	40.0;36.2 ⬇️	40.3;35.7	40h	+GN MODE_FPN=True FPN.NORM=GN BACKBONE.NORM=GN FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head
R101-C4	41.4;35.2 ⬇️		60h	standard BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]
R101-FPN	40.4;36.6 ⬇️	40.9;36.4	38h	standard MODE_FPN=True BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]
R101-FPN	46.5;40.1 ⬇️ 3		73h	+++ MODE_FPN=True FPN.CASCADE=True BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3] TEST.RESULT_SCORE_THRESH=1e-4 PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800] TRAIN.LR_SCHEDULE=[420000,500000,540000]

1: Here we comapre models that have identical training & inference cost between the two implementation. However their numbers are different due to many small implementation details.

2: Numbers taken from Group Normalization

3: Our mAP is 10+ point better than the official model in matterport/Mask_RCNN with the same R101-FPN backbone.

Notes

NOTES.md has some notes about implementation details & speed.