object_detection

Object Detection

Example to train a object detection model as described in Ren et al. (2015), Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Detection Performance

The architecture of sightseq object detection is equal to torchvision apart from spliting the computation of criterion from the network's forward propagation graph to fasterrcnn_loss. So the models is backwards-compatible with torchvison. And I borrow the torchvision fasterrcnn_resnet50_fpn weights to sightseq, the mAP is exactly the same as torchvision.

Run eval, you can get the mAP on the coco_2017_val. (Assume coco dataset in [YOUR_SIGHTSEQ_ROOT]/data-bin/coco)

python -m examples.object_detection.eval

Model	box AP
fasterrcnn_resnet50_fpn	0.37

Installation Requirements

• python >= 3.5
• pytorch >= 1.1.0
• torchvision >= 0.3.0
• cocoapi

cocoapi step-by-step installation:

# install pycocotools
# cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

Training new models

Navigate (cd) to the root of the toolbox [YOUR_SIGHTSEQ_ROOT].

Prepare the data

You can pretrain Faster RCNN from scratch, you should prepare the training data using coco format, and it's recommended to symlink the dataset root to ./data-bin.

.
├── data-bin
│   └── coco
│       ├── annotations
│       ├── train2017
│       ├── val2017
│       └── test2017
├── examples
└── sightseq

Training

Note, currently the data sampler with aspect ratio group is not ensembled to sightseq when training, but I keep this parameter for later use.

python -m sightseq.train [DATA] \
    --task object_detection \
    --num-classes 91 \
    --arch fasterrcnn_resnet50_fpn \
    --criterion fasterrcnn_loss \
    --optimizer sgd \
    --lr 0.02 --momentum 0.9 --weight-decay 1e-4 \
    --batch-size 2 \
    --valid-subset val \
    --max-epoch 26 \
    --aspect-ratio-group-factor 3 \
    --no-progress-bar

Testing

python -m sightseq.generate_coco [DATA] \
    --task object_detection \
    --criterion fasterrcnn_loss \
    --batch-size 1 --gen-subset val \
    --path [CHECKPOINTS_PATH]