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CenterMask_plus(under construction)

Note: The original implementation is at here. More details could be found in their project. this project is still contructing, models are still running on my server, once got some results, I will update this project.

Highlights

  • LOSS . various loss functions(DistanceIOU, CompleteIOU) will be supplied
  • DATASET . interfaces to more driving datasets(including KITTI, Cityscapes)
  • ATSS. a new proposed idea(ATSS) based on FCOS will be included
  • Tensorboard. to display losses

Installation

Check INSTALL.md for installation instructions which is orginate from maskrcnn-benchmark.

Set up datasets

  1. Download the Cityscapes dataset (leftImg8bit_trainvaltest.zip) from the official website.
  2. Download the annotation files from the official website.
  3. Organize the dataset as the following structure: 
    ├── /path/to/cityscapes
│   ├── annotations
│   ├── leftImg8bit
│   ├── gtFine
  4. Create a soft link(optional): 
    ROOT=/path/to/cityscapes
cd $ROOT/datasets
ln -s /path/to/cityscapes cityscapes

Kitti

  1. Download the Kitti dataset from the official website.
  2. Download the annotation file instances_train.json and instances_val.json from Kins.
  3. Organize the dataset as the following structure: 
    ├── /path/to/kitti
│   ├── testing
│   │   ├── image_2
│   │   ├── instance_val.json
│   ├── training
│   │   ├── image_2
│   │   ├── instance_train.json
  4. Create a soft link: 
    ROOT=/path/to/kitti
cd $ROOT/datasets
ln -s /path/to/kitti kitti

Tips

when trying to use these new datasets, three things should be done before launching your project.

  1. add your dataloader and eval scripts
  2. modifiy paths_catalog.py to add directories of your new datasets
  3. change the INPUT and NUM_CLASSES in defaults.py

Training

Follow the instructions of maskrcnn-benchmark guides.

If you want multi-gpu (e.g.,8) training,

export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py --config-file "configs/centermask/centermask_R_50_FPN_1x.yaml"

Evaluation

Follow the instruction of maskrcnn-benchmark

First of all, you have to download the weight file you want to inference.

multi-gpu evaluation & test batch size 16,
wget https://www.dropbox.com/s/2enqxenccz4xy6l/centermask-lite-R-50-ms-bs32-1x.pth
export NGPUS=8
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py --config-file "configs/centermask/centermask_R_50_FPN_lite_res600_ms_bs32_1x.yaml"   TEST.IMS_PER_BATCH 16 MODEL.WEIGHT centermask-lite-R-50-ms-bs32-1x.pth

FCOS on COCO2017val

coco val2017 results

Detector Backbone epoch Mask AP (AP/APs/APm/APl) Box AP (AP/APs/APm/APl) Time (ms) Weight
Mask R-CNN R-50-FPN 24 35.9/17.1/38.9/52.0 39.7/24.0/43.0/50.8 77 link
FCOS R-50-FPN 24 33.4/18.4/36.4/41.8
CenterMask V2-39-FPN 24 37.7/17.9/40.8/54.3 42.6/25.3/46.3/55.2 70 link
Mask R-CNN R-50-FPN 36 36.5/17.9/39.2/52.5 40.5/24.7/43.7/52.2 77 link
CenterMask R-50-FPN 36 37.0/17.6/39.7/53.8 41.7/24.8/45.1/54.5 72 link
CenterMask V2-39-FPN 36 38.5/19.0/41.5/54.7 43.5/27.1/46.9/55.9 70 link
Mask R-CNN R-101-FPN 24 37.8/18.5/40.7/54.9 42.2/25.8/45.8/54.0 94 link
CenterMask R-101-FPN 24 38.0/18.2/41.3/55.2 43.1/25.7/47.0/55.6 91 link
CenterMask V2-57-FPN 24 38.5/18.6/41.9/56.2 43.8/26.7/47.4/57.1 76 link
Mask R-CNN R-101-FPN 36 38.0/18.4/40.8/55.2 42.4/25.4/45.5/55.2 94 link
CenterMask R-101-FPN 36 38.6/19.2/42.0/56.1 43.7/27.2/47.6/56.7 91 link
CenterMask V2-57-FPN 36 39.4/19.6/42.9/55.9 44.6/27.7/48.3/57.3 76 link
Mask R-CNN X-101-32x8d-FPN 24 38.9/19.6/41.6/55.7 43.7/27.6/46.9/55.9 165 link
CenterMask X-101-32x8d-FPN 24 39.1/19.6/42.5/56.1 44.3/26.9/48.5/57.0 157 link
CenterMask V2-99-FPN 24 39.6/19.6/43.1/56.9 44.8/27.6/49.0/57.7 106 link
Mask R-CNN X-101-32x8d-FPN 36 38.6/19.7/41.1/55.2 43.6/27.3/46.7/55.6 165 link
CenterMask X-101-32x8d-FPN 36 39.1/18.5/42.3/56.4 44.4/26.7/47.7/57.1 157 link
CenterMask V2-99-FPN 36 40.2/20.6/43.5/57.3 45.6/29.2/49.3/58.8 106 link

Note that the all models are trained using train-time augmentation (multi-scale).
The inference time of all models is measured on Titan Xp GPU.
24/36 epoch are same as x2/x3 training schedule in detectron, respectively.

For single-gpu evaluation & test batch size 1,
wget https://www.dropbox.com/s/2enqxenccz4xy6l/centermask-lite-R-50-ms-bs32-1x.pth
CUDA_VISIBLE_DEVICES=0
python tools/test_net.py --config-file "configs/centermask/centermask_R_50_FPN_lite_res600_ms_bs32_1x.yaml" TEST.IMS_PER_BATCH 1 MODEL.WEIGHT centermask-lite-R-50-ms-bs32-1x.pth

Tensorbaord

in order to visualize our losses, please install tensorboardX by typing pip install tensorboardX

TODO

  • [] add ATSS provided by a CVPR2020
  • [] add PointRend
  • add more different loss functions