FasterRCNN

Faster R-CNN / Mask R-CNN on COCO

This example provides a minimal (2k lines) and faithful implementation of the following object detection / instance segmentation 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 / multi-node distributed training, multi-GPU evaluation
• Cross-GPU BatchNorm (aka Sync-BN, from MegDet: A Large Mini-Batch Object Detector)
• Group Normalization
• Training from scratch (from Rethinking ImageNet Pre-training)

This is likely the best-performing open source TensorFlow reimplementation of the above papers.

Dependencies

• Python 3.3+; OpenCV
• TensorFlow ≥ 1.6
• 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?/
    # image files that are mentioned in the corresponding json
  val201?/
    # image files that are mentioned in corresponding json

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:

To 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-AlignPadding.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). Some reasonable configurations are listed in the table below.

Inference:

To predict on an image (needs DISPLAY to show the outputs):

./predict.py --predict input1.jpg input2.jpg --load /path/to/Trained-Model-Checkpoint --config SAME-AS-TRAINING

To evaluate the performance of a model on COCO:

./predict.py --evaluate output.json --load /path/to/Trained-Model-Checkpoint \
    --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 configs used in training.

Results

These models are trained on trainval35k and evaluated on minival2014 using mAP@IoU=0.50:0.95. All models are fine-tuned from ImageNet pre-trained R50/R101 models in tensorpack model zoo, unless otherwise noted. All models are trained with 8 NVIDIA V100s, unless otherwise noted.

Performance in Detectron can be approximately reproduced.

Backbone	mAP (box;mask)	Detectron mAP 1 (box;mask)	Time (on 8 V100s)	Configurations (click to expand)
R50-C4	33.5		17h	super quick MODE_MASK=False FRCNN.BATCH_PER_IM=64 PREPROC.TRAIN_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	23h	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	38.4;35.1 ⬇️	38.6;34.5	27h	standard MODE_FPN=True
R50-FPN	42.0;36.3		36h	+Cascade MODE_FPN=True FPN.CASCADE=True
R50-FPN	39.5;35.2	39.5;34.42	31h	+ConvGNHead MODE_FPN=True FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head
R50-FPN	40.0;36.2 ⬇️	40.3;35.7	33h	+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_BLOCKS=[3,4,23,3]
R101-FPN	40.4;36.6 ⬇️	40.9;36.4	37h	standard MODE_FPN=True BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]
R101-FPN	46.5;40.1 ⬇️ 3		73h	3x+Cascade+TrainAug MODE_FPN=True FPN.CASCADE=True BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3] TEST.RESULT_SCORE_THRESH=1e-4 PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800] TRAIN.LR_SCHEDULE=[420000,500000,540000]
R101-FPN (From Scratch)	47.5;41.2 ⬇️	47.4;40.54	45h (on 48 V100s)	9x+GN+Cascade+TrainAug MODE_FPN=True FPN.CASCADE=True BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3] FPN.NORM=GN BACKBONE.NORM=GN FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800] TRAIN.LR_SCHEDULE=[1500000,1580000,1620000] BACKBONE.FREEZE_AT=0

1: Numbers taken from Detectron Model Zoo. We compare models that have identical training & inference cost between the two implementations. Their numbers can be different due to many small implementation details. For example, our FPN models are sometimes slightly worse in box AP, which is mainly due to batch size.

2: Numbers taken from Table 5 in Group Normalization

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

4: This entry does not use ImageNet pre-training. Detectron numbers are taken from Fig. 5 in Rethinking ImageNet Pre-training. Note that our training strategy is slightly different: we enable cascade throughout the entire training.

Notes

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