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basemodel.py
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coco.py
coco.py
 
 
common.py
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config.py
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README.md

Faster-RCNN / Mask-RCNN on COCO

This example aims to provide a minimal (1.3k lines) implementation of end-to-end Faster-RCNN & Mask-RCNN (with ResNet backbones) on COCO.

Dependencies

  • Python 3; TensorFlow >= 1.4.0
  • pycocotools, OpenCV.
  • Pre-trained ResNet model from tensorpack model zoo.
  • COCO data. It assumes the following directory structure:
DIR/
  annotations/
    instances_train2014.json
    instances_val2014.json
    instances_minival2014.json
    instances_valminusminival2014.json
  train2014/
    COCO_train2014_*.jpg
  val2014/
    COCO_val2014_*.jpg

minival and valminusminival are optional. You can download them here.

Usage

Change config in config.py:

  1. Change BASEDIR to /path/to/DIR as described above.
  2. Change MODE_MASK to switch Faster-RCNN or Mask-RCNN.

Train:

./train.py --load /path/to/ImageNet-ResNet50.npz

The code is only for training with 1, 2, 4 or 8 GPUs. Otherwise, you probably need different hyperparameters for the same performance.

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

./train.py --predict input.jpg --load /path/to/model

Evaluate the performance of a model and save to json. (Pretrained models can be downloaded in model zoo:

./train.py --evaluate output.json --load /path/to/model

Results

These models are trained with different configurations on trainval35k and evaluated on minival using mAP@IoU=0.50:0.95. MaskRCNN results contain both bbox and segm mAP.

Backbone FASTRCNN_BATCH resolution schedule mAP (bbox/segm) Time
R-50 64 (600, 1024) 280k 33.0 22h on 8 P100
R-50 512 (800, 1333) 280k 35.6 55h on 8 P100
R-50 512 (800, 1333) 360k 36.7 49h on 8 V100
R-50 256 (800, 1333) 280k 36.9/32.3 39h on 8 P100
R-50 512 (800, 1333) 360k 37.7/33.0 72h on 8 P100
R-101 512 (800, 1333) 280k 40.1/34.4 70h on 8 P100

The two 360k models have identical configurations with R50-C4-2x configuration in Detectron Model Zoo. They get the same performance with the official models, and are about 14% slower than the official implementation, probably due to the lack of specialized ops (e.g. AffineChannel, ROIAlign) in TensorFlow.

Notes

See Notes on This Implementation