RectLabel

An offline image annotation tool for object detection and segmentation.

To download RectLabel apps.

Post the problem to our Github issues.

Have questions? Send an email to support@rectlabel.com.

Thank you.

Train RLE masks using a Mask R-CNN model on Detectron2

Detectron2 is Facebook AI Research’s next generation library that provides state-of-the-art detection and segmentation algorithms.

Install CUDA, cuDNN, and PyTorch.

Install Detectron2.

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

Download training/inference scripts.

wget https://huggingface.co/rectlabel/detectron2/resolve/main/detectron2_scripts.zip
unzip detectron2_scripts.zip
mv detectron2_scripts/my_train_rle.py detectron2/tools
mv detectron2_scripts/my_predictor_segmentation.py detectron2/demo
mv detectron2_scripts/visualizer.py detectron2/detectron2/utils

Download donuts dataset.

wget https://huggingface.co/datasets/rectlabel/datasets/resolve/main/donuts.zip
unzip donuts.zip
mv donuts detectron2/demo

To label your custom dataset, use Edit menus.

To export your custom dataset, use Export menus.

Export COCO JSON file

This is the training script.

import os

from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.data.datasets import register_coco_instances
from detectron2.engine import DefaultTrainer
from detectron2.evaluation import COCOEvaluator

class Trainer(DefaultTrainer):
    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        return COCOEvaluator(dataset_name, output_folder)

def main():
    images_path = "donuts/images"
    annotations_path = "donuts/coco_labels_rle.json"
    config_name = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
    register_coco_instances("dataset_train", {}, annotations_path, images_path)
    cfg = get_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(config_name))
    cfg.DATASETS.TRAIN = ("dataset_train",)
    cfg.DATASETS.TEST = ()
    cfg.DATALOADER.NUM_WORKERS = 2
    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_name)
    cfg.SOLVER.IMS_PER_BATCH = 2
    cfg.SOLVER.BASE_LR = 0.001
    cfg.SOLVER.MAX_ITER = 2000 
    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
    # cfg.MODEL.DEVICE = "cpu"
    cfg.INPUT.MASK_FORMAT = "bitmask"
    os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=True)
    trainer.train()

if __name__ == "__main__":
    main()

Run the training script.

cd detectron2/demo
python ../tools/my_train_rle.py

This is the inference script.

import cv2
import glob
import os

from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.data.datasets import register_coco_instances
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer

def main():
    images_path = "donuts/images"
    config_name = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
    MetadataCatalog.get("dataset_train").set(thing_classes=["donut"])
    cfg = get_cfg()
    cfg.merge_from_file(model_zoo.get_config_file(config_name))
    cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
    cfg.SOLVER.IMS_PER_BATCH = 1
    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
    # cfg.MODEL.DEVICE = "cpu"
    predictor = DefaultPredictor(cfg)
    os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
    image_paths = glob.glob(os.path.join(images_path, "*.jpg"))
    for image_path in image_paths:
        image = cv2.imread(image_path)
        outputs = predictor(image)
        v = Visualizer(image[:, :, ::-1], MetadataCatalog.get("dataset_train"), scale=1.2)
        out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
        output_path = os.path.join(cfg.OUTPUT_DIR, os.path.basename(image_path))
        cv2.imwrite(output_path, out.get_image()[:, :, ::-1])

if __name__ == "__main__":
    main()

Run the inference script.

cd detectron2/demo
python my_predictor_segmentation.py

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