SOLOV2_FSOLO

The model is best mask

INSTALLATION

Requirements

conda creat --name [Your envirement name] python=3.7

First install Detectron2 following the official guide: refer INSTALL.md:

Install some libary.

python -m pip install pyyaml==5.1

Install cuda 11.1 + torch 1.9.

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

Install Detectron2 from source follwing the local clone. To rebuild detectron2 that's built from a local clone, use rm -rf build/ **/*.so to clean the old build first. You often need to rebuild detectron2 after reinstalling PyTorch.

rm -rf build/ **/*.so

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

Then build AdelaiDet

git clone https://github.com/aim-uofa/AdelaiDet.git
cd AdelaiDet
python setup.py build develop

Some projects may require special setup, please follow their own README.md in configs

Quick Start.

Inference with Pre-Trained Models.

1. Pick a model and its config file, for example, fcos_R_50_1x.yaml.
2. Download the model wget https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download -O fcos_R_50_1x.pth.
3. Run the demo with

wget https://cloudstor.aarnet.edu.au/plus/s/chF3VKQT4RDoEqC/download -O SOLOv2_R50_3x.pth
python demo/demo.py \
    --config-file configs/SOLOv2/R50_3x.yaml \
    --input input1.jpg input2.jpg \
    --opts MODEL.WEIGHTS SOLOv2_R50_3x.pth

Train Your Own Models.

To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md, then run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/SOLOv2/R50_3x.yaml \
    --num-gpus 8 \
    OUTPUT_DIR training_dir/SOLOv2_R50_3x

To evaluate the model after training, run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/SOLOv2/R50_3x.yaml \
    --eval-only \
    --num-gpus 8 \
    OUTPUT_DIR training_dir/SOLOv2_R50_3x \
    MODEL.WEIGHTS training_dir/SOLOv2_R50_3x/model_final.pth

Note That:

• The configs are made for 8-GPU training. To train on another number of GPUs, change the --num-gpus.
• If you want to measure the inference time, please change --num-gpus to 1.
• We set OMP_NUM_THREADS=1 by default, which achieves the best speed on our machines, please change it as needed.
• This quick start is made for FCOS. If you are using other projects, please check the projects' own README.md in configs.