Cascade R-CNN

Model description

In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN.

Step 1: Installation

Cascade R-CNN model is using MMDetection toolbox. Before you run this model, you need to setup MMDetection first.

# Go to "toolbox/MMDetection" directory in root path
cd ../../../../toolbox/MMDetection/
bash install_toolbox_mmdetection.sh

Step 2: Preparing datasets

Go to visit COCO official website, then select the COCO dataset you want to download.

Take coco2017 dataset as an example, specify /path/to/coco2017 to your COCO path in later training process, the unzipped dataset path structure sholud look like:

coco2017
├── annotations
│   ├── instances_train2017.json
│   ├── instances_val2017.json
│   └── ...
├── train2017
│   ├── 000000000009.jpg
│   ├── 000000000025.jpg
│   └── ...
├── val2017
│   ├── 000000000139.jpg
│   ├── 000000000285.jpg
│   └── ...
├── train2017.txt
├── val2017.txt
└── ...

Step 3: Training

# Make soft link to dataset
cd mmdetection/
mkdir -p data/
ln -s /path/to/coco2017 data/coco

# On single GPU
python3 tools/train.py  configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py 

# Multiple GPUs on one machine
bash tools/dist_train.sh  configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py  8

Results

Reference

• Cascade R-CNN: High Quality Object Detection and Instance Segmentation