代码地址：https://github.com/Xiu7/mask-r-cnn
第一步之前，你需要安装pycocotools等工具包。

第一步，建立文件夹，标注格式采用soft-1，soft-2

文件夹列表如下：

1. labelme/total2019下存放labelme生成的json文件，images/total2019下存放图片，其他文件夹先不用管
2. 下图时整体目录，后边建py文件参考
3. 注意一张图片里的多个同类目标采用soft-1,soft-2,soft-3,这类方式命名

第二步，通过creat_txt.py生成val2019.txt，train2019.txt，test2019.txt

程序如下，通过trainval_percent ，train_percent 参数设置train，test，val数据集的比例，程序直接运行

# !/usr/bin/python
# -*- coding: utf-8 -*-
import os
import random

trainval_percent = 0.8  # 验证集+训练集占总比例多少
train_percent = 0.7  # 训练数据集占验证集+训练集比例多少
jsonfilepath = 'labelme/total2019'
txtsavepath = './'
total_xml = os.listdir(jsonfilepath)

num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)

ftrainval = open('./trainval2019.txt', 'w')
ftest = open('./test2019.txt', 'w')
ftrain = open('./train2019.txt', 'w')
fval = open('./val2019.txt', 'w')

for i in list:
    name = total_xml[i][:-5] + 'n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)

ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

生成后txt文件后文件结构如下：

第三步，通过classify.py程序将json文件与图片分类

代码如下，程序直接运行

import shutil
import cv2 as cv

sets=['train2019',  'val2019', 'test2019']
for image_set in sets:
    image_ids = open('./%s.txt'%(image_set)).read().strip().split()
    for image_id in image_ids:
        img = cv.imread('images/total2019/%s.jpg' % (image_id))
        json='labelme/total2019/%s.json'% (image_id)
        cv.imwrite('images/%s/%s.jpg' % (image_set,image_id), img)
        cv.imwrite('labelme/%s/%s.jpg' % (image_set,image_id), img)
        shutil.copy(json,'labelme/%s/%s.json' % (image_set,image_id))
print("完成")

第五步,通过labelme2coco.py生成train2019.json，test2019.json,val2019.json

在根目录下建立labels.txt文件，内容首行为__ignore__,后续为你的分类标签。样例如下

建立labelme2coco.py文件，代码如下

#!/usr/bin/env python

import argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import numpy as np
import PIL.Image
import labelme

try:
    import pycocotools.mask
except ImportError:
    print('Please install pycocotools:nn    pip install pycocotoolsn')
    sys.exit(1)


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument('--input_dir', help='input annotated directory')
    parser.add_argument('--output_dir', help='output dataset directory')
    parser.add_argument('--filename', help='output filename')
    parser.add_argument('--labels', help='labels file', required=True)
    args = parser.parse_args()

    if osp.exists(args.output_dir):
        print('Output directory already exists:', args.output_dir)
        sys.exit(1)
    os.makedirs(args.output_dir)
    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
    print('Creating dataset:', args.output_dir)

    now = datetime.datetime.now()

    data = dict(
        info=dict(
            description=None,
            url=None,
            version=None,
            year=now.year,
            contributor=None,
            date_created=now.strftime('%Y-%m-%d %H:%M:%S.%f'),
        ),
        licenses=[dict(
            url=None,
            id=0,
            name=None,
        )],
        images=[
            # license, url, file_name, height, width, date_captured, id
        ],
        type='instances',
        annotations=[
            # segmentation, area, iscrowd, image_id, bbox, category_id, id
        ],
        categories=[
            # supercategory, id, name
        ],
    )

    class_name_to_id = {}
    for i, line in enumerate(open(args.labels).readlines()):
        class_id = i - 1  # starts with -1
        class_name = line.strip()
        if class_id == -1:
            assert class_name == '__ignore__'
            continue
        class_name_to_id[class_name] = class_id
        data['categories'].append(dict(
            supercategory=None,
            id=class_id,
            name=class_name,
        ))

    out_ann_file = osp.join(args.output_dir,  args.filename+'.json')
    label_files = glob.glob(osp.join(args.input_dir, '*.json'))
    for image_id, label_file in enumerate(label_files):
        print('Generating dataset from:', label_file)
        with open(label_file) as f:
            label_data = json.load(f)

        base = osp.splitext(osp.basename(label_file))[0]
        out_img_file = osp.join(
            args.output_dir, 'JPEGImages', base + '.jpg'
        )
        path=label_data['imagePath'].split("\")

        img_file = osp.join(
            osp.dirname(label_file), path[2]
        )
        img = np.asarray(PIL.Image.open(img_file))
        PIL.Image.fromarray(img).save(out_img_file)
        data['images'].append(dict(
            license=0,
            url=None,
            file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),
            height=img.shape[0],
            width=img.shape[1],
            date_captured=None,
            id=image_id,
        ))

        masks = {}                                     # for area
        segmentations = collections.defaultdict(list)  # for segmentation
        for shape in label_data['shapes']:
            points = shape['points']
            label = shape['label']
            shape_type = shape.get('shape_type', None)
            mask = labelme.utils.shape_to_mask(
                img.shape[:2], points, shape_type
            )

            if label in masks:
                masks[label] = masks[label] | mask
            else:
                masks[label] = mask

            points = np.asarray(points).flatten().tolist()
            segmentations[label].append(points)

        for label, mask in masks.items():
            cls_name = label.split('-')[0]
            if cls_name not in class_name_to_id:
                continue
            cls_id = class_name_to_id[cls_name]

            mask = np.asfortranarray(mask.astype(np.uint8))
            mask = pycocotools.mask.encode(mask)
            area = float(pycocotools.mask.area(mask))
            bbox = pycocotools.mask.toBbox(mask).flatten().tolist()

            data['annotations'].append(dict(
                id=len(data['annotations']),
                image_id=image_id,
                category_id=cls_id,
                segmentation=segmentations[label],
                area=area,
                bbox=bbox,
                iscrowd=0,
            ))

    with open(out_ann_file, 'w') as f:
        json.dump(data, f)


if __name__ == '__main__':
    main()

运行三次labelme2coco.py文件，在annotations下生成coco格式的文件，指令如下

python ./labelme2coco.py --input_dir ./labelme/train2019 --output_dir ./annotations/train2019 --filename instances_train2019 --labels labels.txt

python ./labelme2coco.py --input_dir ./labelme/val2019 --output_dir ./annotations/val2019 --filename instances_val2019 --labels labels.txt

python ./labelme2coco.py --input_dir ./labelme/test2019 --output_dir ./annotations/test2019 --filename instances_test2019 --labels labels.txt

可视化

在coco文件夹下建立coco.py与inspect_data.py
coco.py代码如下

"""
Mask R-CNN
Configurations and data loading code for MS COCO.

Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla

------------------------------------------------------------

Usage: import the module (see Jupyter notebooks for examples), or run from
       the command line as such:

    # Train a new model starting from pre-trained COCO weights
    python3 coco.py train --dataset=/path/to/coco/ --model=coco

    # Train a new model starting from ImageNet weights. Also auto download COCO dataset
    python3 coco.py train --dataset=/path/to/coco/ --model=imagenet --download=True

    # Continue training a model that you had trained earlier
    python3 coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5

    # Continue training the last model you trained
    python3 coco.py train --dataset=/path/to/coco/ --model=last

    # Run COCO evaluatoin on the last model you trained
    python3 coco.py evaluate --dataset=/path/to/coco/ --model=last
"""

import os
import sys
import time
import numpy as np
import imgaug  # https://github.com/aleju/imgaug (pip3 install imgaug)

# Download and install the Python COCO tools from https://github.com/waleedka/coco
# That's a fork from the original https://github.com/pdollar/coco with a bug
# fix for Python 3.
# I submitted a pull request https://github.com/cocodataset/cocoapi/pull/50
# If the PR is merged then use the original repo.
# Note: Edit PythonAPI/Makefile and replace "python" with "python3".
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils

import zipfile
import urllib.request
import shutil

# Root directory of the project
ROOT_DIR = os.path.abspath("model")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils

# Path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
DEFAULT_DATASET_YEAR = "2019"

############################################################
#  Configurations
############################################################


class CocoConfig(Config):
    """Configuration for training on MS COCO.
    Derives from the base Config class and overrides values specific
    to the COCO dataset.
    """
    # Give the configuration a recognizable name
    NAME = "coco"

    # We use a GPU with 12GB memory, which can fit two images.
    # Adjust down if you use a smaller GPU.
    IMAGES_PER_GPU = 1

    # Uncomment to train on 8 GPUs (default is 1)
    # GPU_COUNT = 8

    # Number of classes (including background)
    NUM_CLASSES = 1 + 6  # COCO has 80 classes


############################################################
#  Dataset
############################################################

class CocoDataset(utils.Dataset):
    def load_coco(self, dataset_dir, subset, year=DEFAULT_DATASET_YEAR, class_ids=None,
                  class_map=None, return_coco=False, auto_download=False):
        """Load a subset of the COCO dataset.
        dataset_dir: The root directory of the COCO dataset.
        subset: What to load (train, val, minival, valminusminival)
        year: What dataset year to load (2014, 2017) as a string, not an integer
        class_ids: If provided, only loads images that have the given classes.
        class_map: TODO: Not implemented yet. Supports maping classes from
            different datasets to the same class ID.
        return_coco: If True, returns the COCO object.
        auto_download: Automatically download and unzip MS-COCO images and train
        """

        if auto_download is True:
            self.auto_download(dataset_dir, subset, year)

        coco = COCO("{}/train2019/instances_{}{}.json".format(dataset_dir, subset, year))
        if subset == "minival" or subset == "valminusminival":
            subset = "val"
        image_dir = "{}/{}{}".format(dataset_dir, subset, year)

        # Load all classes or a subset?
        if not class_ids:
            # All classes
            class_ids = sorted(coco.getCatIds())

        # All images or a subset?
        if class_ids:
            image_ids = []
            for id in class_ids:
                image_ids.extend(list(coco.getImgIds(catIds=[id])))
            # Remove duplicates
            image_ids = list(set(image_ids))
        else:
            # All images
            image_ids = list(coco.imgs.keys())

        # Add classes
        for i in class_ids:
            self.add_class("coco", i, coco.loadCats(i)[0]["name"])

        # Add images
        for i in image_ids:
            self.add_image(
                "coco", image_id=i,
                path=os.path.join(image_dir, coco.imgs[i]['file_name']),
                width=coco.imgs[i]["width"],
                height=coco.imgs[i]["height"],
                annotations=coco.loadAnns(coco.getAnnIds(
                    imgIds=[i], catIds=class_ids, iscrowd=None)))
        if return_coco:
            return coco

    def auto_download(self, dataDir, dataType, dataYear):
        """Download the COCO dataset/train if requested.
        dataDir: The root directory of the COCO dataset.
        dataType: What to load (train, val, minival, valminusminival)
        dataYear: What dataset year to load (2014, 2017) as a string, not an integer
        Note:
            For 2014, use "train", "val", "minival", or "valminusminival"
            For 2017, only "train" and "val" train are available
        """

        # Setup paths and file names
        if dataType == "minival" or dataType == "valminusminival":
            imgDir = "{}/{}{}".format(dataDir, "val", dataYear)
            imgZipFile = "{}/{}{}.zip".format(dataDir, "val", dataYear)
            imgURL = "http://images.cocodataset.org/zips/{}{}.zip".format("val", dataYear)
        else:
            imgDir = "{}/{}{}".format(dataDir, dataType, dataYear)
            imgZipFile = "{}/{}{}.zip".format(dataDir, dataType, dataYear)
            imgURL = "http://images.cocodataset.org/zips/{}{}.zip".format(dataType, dataYear)
        # print("Image paths:"); print(imgDir); print(imgZipFile); print(imgURL)

        # Create main folder if it doesn't exist yet
        if not os.path.exists(dataDir):
            os.makedirs(dataDir)

        # Download images if not available locally
        if not os.path.exists(imgDir):
            os.makedirs(imgDir)
            print("Downloading images to " + imgZipFile + " ...")
            with urllib.request.urlopen(imgURL) as resp, open(imgZipFile, 'wb') as out:
                shutil.copyfileobj(resp, out)
            print("... done downloading.")
            print("Unzipping " + imgZipFile)
            with zipfile.ZipFile(imgZipFile, "r") as zip_ref:
                zip_ref.extractall(dataDir)
            print("... done unzipping")
        print("Will use images in " + imgDir)

        # Setup train data paths
        annDir = "{}/train".format(dataDir)
        if dataType == "minival":
            annZipFile = "{}/instances_minival2014.json.zip".format(dataDir)
            annFile = "{}/instances_minival2014.json".format(annDir)
            annURL = "https://dl.dropboxusercontent.com/s/o43o90bna78omob/instances_minival2014.json.zip?dl=0"
            unZipDir = annDir
        elif dataType == "valminusminival":
            annZipFile = "{}/instances_valminusminival2014.json.zip".format(dataDir)
            annFile = "{}/instances_valminusminival2014.json".format(annDir)
            annURL = "https://dl.dropboxusercontent.com/s/s3tw5zcg7395368/instances_valminusminival2014.json.zip?dl=0"
            unZipDir = annDir
        else:
            annZipFile = "{}/annotations_trainval{}.zip".format(dataDir, dataYear)
            annFile = "{}/instances_{}{}.json".format(annDir, dataType, dataYear)
            annURL = "http://images.cocodataset.org/annotations/annotations_trainval{}.zip".format(dataYear)
            unZipDir = dataDir
        # print("Annotations paths:"); print(annDir); print(annFile); print(annZipFile); print(annURL)

        # Download train if not available locally
        if not os.path.exists(annDir):
            os.makedirs(annDir)
        if not os.path.exists(annFile):
            if not os.path.exists(annZipFile):
                print("Downloading zipped train to " + annZipFile + " ...")
                with urllib.request.urlopen(annURL) as resp, open(annZipFile, 'wb') as out:
                    shutil.copyfileobj(resp, out)
                print("... done downloading.")
            print("Unzipping " + annZipFile)
            with zipfile.ZipFile(annZipFile, "r") as zip_ref:
                zip_ref.extractall(unZipDir)
            print("... done unzipping")
        print("Will use train in " + annFile)

    def load_mask(self, image_id):
        """Load instance masks for the given image.

        Different datasets use different ways to store masks. This
        function converts the different mask format to one format
        in the form of a bitmap [height, width, instances].

        Returns:
        masks: A bool array of shape [height, width, instance count] with
            one mask per instance.
        class_ids: a 1D array of class IDs of the instance masks.
        """
        # If not a COCO image, delegate to parent class.
        image_info = self.image_info[image_id]
        if image_info["source"] != "coco":
            return super(CocoDataset, self).load_mask(image_id)

        instance_masks = []
        class_ids = []
        annotations = self.image_info[image_id]["annotations"]
        # Build mask of shape [height, width, instance_count] and list
        # of class IDs that correspond to each channel of the mask.
        for annotation in annotations:
            class_id = self.map_source_class_id(
                "coco.{}".format(annotation['category_id']))
            if class_id:
                m = self.annToMask(annotation, image_info["height"],
                                   image_info["width"])
                # Some objects are so small that they're less than 1 pixel area
                # and end up rounded out. Skip those objects.
                if m.max() < 1:
                    continue
                # Is it a crowd? If so, use a negative class ID.
                if annotation['iscrowd']:
                    # Use negative class ID for crowds
                    class_id *= -1
                    # For crowd masks, annToMask() sometimes returns a mask
                    # smaller than the given dimensions. If so, resize it.
                    if m.shape[0] != image_info["height"] or m.shape[1] != image_info["width"]:
                        m = np.ones([image_info["height"], image_info["width"]], dtype=bool)
                instance_masks.append(m)
                class_ids.append(class_id)

        # Pack instance masks into an array
        if class_ids:
            mask = np.stack(instance_masks, axis=2).astype(np.bool)
            class_ids = np.array(class_ids, dtype=np.int32)
            return mask, class_ids
        else:
            # Call super class to return an empty mask
            return super(CocoDataset, self).load_mask(image_id)

    def image_reference(self, image_id):
        """Return a link to the image in the COCO Website."""
        info = self.image_info[image_id]
        if info["source"] == "coco":
            return "http://cocodataset.org/#explore?id={}".format(info["id"])
        else:
            super(CocoDataset, self).image_reference(image_id)

    # The following two functions are from pycocotools with a few changes.

    def annToRLE(self, ann, height, width):
        """
        Convert annotation which can be polygons, uncompressed RLE to RLE.
        :return: binary mask (numpy 2D array)
        """
        segm = ann['segmentation']
        if isinstance(segm, list):
            # polygon -- a single object might consist of multiple parts
            # we merge all parts into one mask rle code
            rles = maskUtils.frPyObjects(segm, height, width)
            rle = maskUtils.merge(rles)
        elif isinstance(segm['counts'], list):
            # uncompressed RLE
            rle = maskUtils.frPyObjects(segm, height, width)
        else:
            # rle
            rle = ann['segmentation']
        return rle

    def annToMask(self, ann, height, width):
        """
        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
        :return: binary mask (numpy 2D array)
        """
        rle = self.annToRLE(ann, height, width)
        m = maskUtils.decode(rle)
        return m


############################################################
#  COCO Evaluation
############################################################

def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks):
    """Arrange resutls to match COCO specs in http://cocodataset.org/#format
    """
    # If no results, return an empty list
    if rois is None:
        return []

    results = []
    for image_id in image_ids:
        # Loop through detections
        for i in range(rois.shape[0]):
            class_id = class_ids[i]
            score = scores[i]
            bbox = np.around(rois[i], 1)
            mask = masks[:, :, i]

            result = {
                "image_id": image_id,
                "category_id": dataset.get_source_class_id(class_id, "coco"),
                "bbox": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],
                "score": score,
                "segmentation": maskUtils.encode(np.asfortranarray(mask))
            }
            results.append(result)
    return results


def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None):
    """Runs official COCO evaluation.
    dataset: A Dataset object with valiadtion data
    eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
    limit: if not 0, it's the number of images to use for evaluation
    """
    # Pick COCO images from the dataset
    image_ids = image_ids or dataset.image_ids

    # Limit to a subset
    if limit:
        image_ids = image_ids[:limit]

    # Get corresponding COCO image IDs.
    coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids]

    t_prediction = 0
    t_start = time.time()

    results = []
    for i, image_id in enumerate(image_ids):
        # Load image
        image = dataset.load_image(image_id)

        # Run detection
        t = time.time()
        r = model.detect([image], verbose=0)[0]
        t_prediction += (time.time() - t)

        # Convert results to COCO format
        # Cast masks to uint8 because COCO tools errors out on bool
        image_results = build_coco_results(dataset, coco_image_ids[i:i + 1],
                                           r["rois"], r["class_ids"],
                                           r["scores"],
                                           r["masks"].astype(np.uint8))
        results.extend(image_results)

    # Load results. This modifies results with additional attributes.
    coco_results = coco.loadRes(results)

    # Evaluate
    cocoEval = COCOeval(coco, coco_results, eval_type)
    cocoEval.params.imgIds = coco_image_ids
    cocoEval.evaluate()
    cocoEval.accumulate()
    cocoEval.summarize()

    print("Prediction time: {}. Average {}/image".format(
        t_prediction, t_prediction / len(image_ids)))
    print("Total time: ", time.time() - t_start)


############################################################
#  Training
############################################################


if __name__ == '__main__':
    import argparse

    # Parse command line arguments
    parser = argparse.ArgumentParser(
        description='Train Mask R-CNN on MS COCO.')
    parser.add_argument("command",
                        metavar="<command>",
                        help="'train' or 'evaluate' on MS COCO")
    parser.add_argument('--dataset', required=True,
                        metavar="/path/to/coco/",
                        help='Directory of the MS-COCO dataset')
    parser.add_argument('--year', required=False,
                        default=DEFAULT_DATASET_YEAR,
                        metavar="<year>",
                        help='Year of the MS-COCO dataset (2014 or 2017) (default=2014)')
    parser.add_argument('--model', required=True,
                        metavar="/path/to/weights.h5",
                        help="Path to weights .h5 file or 'coco'")
    parser.add_argument('--logs', required=False,
                        default=DEFAULT_LOGS_DIR,
                        metavar="/path/to/logs/",
                        help='Logs and checkpoints directory (default=logs/)')
    parser.add_argument('--limit', required=False,
                        default=500,
                        metavar="<image count>",
                        help='Images to use for evaluation (default=500)')
    parser.add_argument('--download', required=False,
                        default=False,
                        metavar="<True|False>",
                        help='Automatically download and unzip MS-COCO files (default=False)',
                        type=bool)
    args = parser.parse_args()
    print("Command: ", args.command)
    print("Model: ", args.model)
    print("Dataset: ", args.dataset)
    print("Year: ", args.year)
    print("Logs: ", args.logs)
    print("Auto Download: ", args.download)

    # Configurations
    if args.command == "train":
        config = CocoConfig()
    else:
        class InferenceConfig(CocoConfig):
            # Set batch size to 1 since we'll be running inference on
            # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
            GPU_COUNT = 1
            IMAGES_PER_GPU = 1
            DETECTION_MIN_CONFIDENCE = 0
        config = InferenceConfig()
    config.display()

    # Create model
    if args.command == "train":
        model = modellib.MaskRCNN(mode="training", config=config,
                                  model_dir=args.logs)
    else:
        model = modellib.MaskRCNN(mode="inference", config=config,
                                  model_dir=args.logs)

    # Select weights file to load
    if args.model.lower() == "coco":
        model_path = COCO_MODEL_PATH
    elif args.model.lower() == "last":
        # Find last trained weights
        model_path = model.find_last()
    elif args.model.lower() == "imagenet":
        # Start from ImageNet trained weights
        model_path = model.get_imagenet_weights()
    else:
        model_path = args.model

    # Load weights
    print("Loading weights ", model_path)
    model.load_weights(model_path, by_name=True)

    # Train or evaluate
    if args.command == "train":
        # Training dataset. Use the training set and 35K from the
        # validation set, as as in the Mask RCNN paper.
        dataset_train = CocoDataset()
        dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download)
        if args.year in '2014':
            dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)
        dataset_train.prepare()

        # Validation dataset
        dataset_val = CocoDataset()
        val_type = "val" if args.year in '2017' else "minival"
        dataset_val.load_coco(args.dataset, val_type, year=args.year, auto_download=args.download)
        dataset_val.prepare()

        # Image Augmentation
        # Right/Left flip 50% of the time
        augmentation = imgaug.augmenters.Fliplr(0.5)

        # *** This training schedule is an example. Update to your needs ***

        # Training - Stage 1
        print("Training network heads")
        model.train(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE,
                    epochs=40,
                    layers='heads',
                    augmentation=augmentation)

        # Training - Stage 2
        # Finetune layers from ResNet stage 4 and up
        print("Fine tune Resnet stage 4 and up")
        model.train(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE,
                    epochs=120,
                    layers='4+',
                    augmentation=augmentation)

        # Training - Stage 3
        # Fine tune all layers
        print("Fine tune all layers")
        model.train(dataset_train, dataset_val,
                    learning_rate=config.LEARNING_RATE / 10,
                    epochs=160,
                    layers='all',
                    augmentation=augmentation)

    elif args.command == "evaluate":
        # Validation dataset
        dataset_val = CocoDataset()
        val_type = "val" if args.year in '2017' else "minival"
        coco = dataset_val.load_coco(args.dataset, val_type, year=args.year, return_coco=True, auto_download=args.download)
        dataset_val.prepare()
        print("Running COCO evaluation on {} images.".format(args.limit))
        evaluate_coco(model, dataset_val, coco, "bbox", limit=int(args.limit))
    else:
        print("'{}' is not recognized. "
              "Use 'train' or 'evaluate'".format(args.command))

inspect_data.py代码如下

import os
import sys
import numpy as np

ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
from mrcnn import visualize

# MS COCO Dataset
import coco
config = coco.CocoConfig()
COCO_DIR = "../annotations"  # TODO: enter value here
# Load dataset
if config.NAME == 'shapes':
    dataset = shapes.ShapesDataset()
    dataset.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
elif config.NAME == "coco":
    dataset = coco.CocoDataset()
    dataset.load_coco(COCO_DIR, "train")

# Must call before using the dataset
dataset.prepare()

print("Image Count: {}".format(len(dataset.image_ids)))
print("Class Count: {}".format(dataset.num_classes))
for i, info in enumerate(dataset.class_info):
    print("{:3}. {:50}".format(i, info['name']))
# Load and display random samples
image_ids = np.random.choice(dataset.image_ids, 3)
print(image_ids)
for image_id in image_ids:
    image = dataset.load_image(image_id)
    mask, class_ids = dataset.load_mask(image_id)
    visualize.display_top_masks(image, mask, class_ids, dataset.class_names)

运行inspect_data.py,即可可视化数据集

训练

第一步，在model中存放mask_rcnn_coco.h5模型
第二步，修改mdf.py参数，87行修改成自己的分类数(mdf.py 就是samples/coco中的coco.py,我重命名了一下)
最后一步，输入python mdf.py train --dataset=./coco_mdf --model=coco开始训练

最后

以上就是含糊蜡烛最近收集整理的关于通过labelme制作coco格式数据集，并使用mask r-cnn训练的全部内容，更多相关通过labelme制作coco格式数据集，并使用mask内容请搜索靠谱客的其他文章。