This repo uses Centernet and Conditional Convolutions for Instance Segmentation
Objects as Points,
CondInst: Conditional Convolutions for Instance Segmentation
These results are taken for CenterSeg model trained for 101 epochs
| type | AP | AP50 | AP75 | APs | APm | APl |
|---|---|---|---|---|---|---|
| box | 0.278 | 0.430 | 0.297 | 0.129 | 0.305 | 0.382 |
| mask | 0.226 | 0.387 | 0.227 | 0.078 | 0.253 | 0.340 |
| type | AR | AR50 | AR75 | ARs | ARm | ARl |
|---|---|---|---|---|---|---|
| box | 0.275 | 0.455 | 0.480 | 0.265 | 0.510 | 0.674 |
| mask | 0.235 | 0.369 | 0.385 | 0.170 | 0.418 | 0.585 |
CenterPoseSeg model not trained yet
This repo supports both CPU and GPU Training and Inference.
git clone --recurse-submodules https://github.com/ajaichemmanam/CenterSeg.git
pip3 install -r requirements.txt
Compile DCN
cd src/lib/models/networks/DCNv2/
python3 setup.py build develop
Compile NMS
cd src/lib/external
python3 setup.py build_ext --inplace
Pre-Release : Google Drive
Download the most recent model (model_last_e101.pth), copy to exp/ctseg/coco_dla_1x/
Rename as model_last.pth
python3 demo.py ctseg --exp_id coco_dla_1x --keep_res --resume --demo ../data/coco/val2017
Note: Model is not completely trained (101 Epochs only). Will update later.
python3 main.py ctseg --exp_id coco_dla_1x --batch_size 10 --master_batch 5 --lr 1.25e-4 --gpus 0 --num_workers 4
python3 main.py ctseg --exp_id coco_dla_1x --batch_size 2 --master_batch -1 --lr 1.25e-4 --gpus -1 --num_workers 4
python3 test.py ctseg --exp_id coco_dla_1x --keep_res --resume
CenterSeg is released under the MIT License (refer to the LICENSE file for details). This repo contains code borrowed from multiple sources. Please see their respective licenses.