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