SOLOv2-detectron2
Unofficial implementation for SOLOv2: Dynamic, Faster and Stronger instance segmentation.
Log
2020/6/12
At present, there are some bugs in the training code, leading to poor performance. Due to the lack of GPU, it is difficult to timely fix these bugs, so we should use it carefully.
2020/6/4
config | bbox | mask | weight |
---|---|---|---|
MS_R_50_2x.yaml | 37.486 | 35.953 | google drive |
There are still a few bugs, "Person" is completely ignored, so performance should be higher than it is now.
Like this:
Now training code has been fixed, and the inference will not be affected.
Install
The code is based on detectron2. Please check Install.md for installation instructions.
Training
Follows the same way as detectron2.
Single GPU:
python train_net.py --config-file configs/MS_R_50_2x.yaml
Multi GPU(for example 8):
python train_net.py --num-gpus 8 --config-file configs/MS_R_50_2x.yaml
Please adjust the IMS_PER_BATCH in the config file according to the GPU memory.
Inference
First replace the original detectron2 installed postprocessing.py with the file.
Single GPU:
python train_net.py --config-file configs/MS_R_50_2x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
Multi GPU(for example 8):
python train_net.py --num-gpus 8 --config-file configs/MS_R_50_2x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
Demo
cd demo/
python demo.py --config-file ../configs/MS_R_50_2x.yaml \
--input /path/to/input_image \
[--other-options]
--opts MODEL.WEIGHTS /path/to/checkpoint_file