SAM requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
Before you start installation, create an environment first:
conda create --name sam python==3.9
Install Segment Anything:
pip install git+https://github.com/facebookresearch/segment-anything.git
or clone the repository locally and install with
git clone [email protected]:rootvisionai/segment-anything.git
cd segment-anything; pip install -e .
The following dependencies are necessary for the FEWSAM:
pip install opencv-python PyYAML PySimpleGUI
Now download the model checkpoints:
More accurate <<< VIT-H | VIT-L | VIT-B >>> Faster
Before you start the application, create a folder to put your support images that will be used to learn from, then create a folder to put your query images that are going to be labeled. Put the relative path to the folders to support_dir and query_dir in config.yml.
python interface.py
then adjust make_request.py according to your images and paths
Finally, run the server ...
python backend/server.py
and make request while server.py is running
python make_request.py
Build docker image
docker build -t few_shot_sam:0.0.1 .
Run docker container
docker run --gpus all -p 80:8080 -it few_shot_sam:0.0.1