mindspore/model_zoo/official/cv/maskrcnn

• Contents
• MaskRCNN Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Run in docker
• Script Description
• • Script and Sample Code
  • Script Parameters
  • • Training Script Parameters
    • Parameters Configuration
  • Training Process
  • • Training
    • Distributed Training
    • Training Result
  • Evaluation Process
  • • Evaluation
    • Evaluation result
  • Model Export
  • Inference Process
  • • Usage
    • result
• Model Description
• • Performance
  • • Evaluation Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• MaskRCNN Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Run in docker
• Script Description
  • Script and Sample Code
  • Script Parameters
    • Training Script Parameters
    • Parameters Configuration
  • Training Process
    • Training
    • Distributed Training
    • Training Result
  • Evaluation Process
    • Evaluation
    • Evaluation Result
• Model Description
  • Performance
    • Evaluation Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

MaskRCNN Description

MaskRCNN is a conceptually simple, flexible, and general framework for object instance segmentation. The approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in
parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing to estimate human poses in the same framework.
It shows top results in all three tracks of the COCO suite of challenges, including instance segmentation, boundingbox object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.

Model Architecture

MaskRCNN is a two-stage target detection network. It extends FasterRCNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.This network uses a region proposal network (RPN), which can share the convolution features of the whole image with the detection network, so that the calculation of region proposal is almost cost free. The whole network further combines RPN and mask branch into a network by sharing the convolution features.

Paper: Kaiming He, Georgia Gkioxari, Piotr Dollar and Ross Girshick. "MaskRCNN"

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

• COCO2017 is a popular dataset with bounding-box and pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning. There are 118K/5K images for train/val.

• Dataset size: 19G

  • Train: 18G, 118000 images
  • Val: 1G, 5000 images
  • Annotations: 241M, instances, captions, person_keypoints, etc.

• Data format: image and json files (Note: Data will be processed in dataset.py)

Environment Requirements

• Hardware（Ascend）

  • Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the application form to ascend@huawei.com. Once approved, you can get the resources.

• Framework

  • MindSpore

• Docker base image

  • Ascend Hub

• For more information, please check the resources below:

  • MindSpore Tutorials
  • MindSpore Python API

• third-party libraries

pip install Cython
pip install pycocotools
pip install mmcv=0.2.14

Quick Start

1. Download the dataset COCO2017.

2. Change the COCO_ROOT and other settings you need in config.py. The directory structure should look like the follows:

   .
   └─cocodataset
     ├─annotations
       ├─instance_train2017.json
       └─instance_val2017.json
     ├─val2017
     └─train2017
   

   If you use your own dataset to train the network, Select dataset to other when run script.
   Create a txt file to store dataset information organized in the way as shown as following:

   train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
   

   Each row is an image annotation split by spaces. The first column is a relative path of image, followed by columns containing box and class information in the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the IMAGE_DIR(dataset directory) and the relative path in ANNO_PATH(the TXT file path), which can be set in config.py.

3. Execute train script.
   After dataset preparation, you can start training as follows:

   # distributed training
   bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT]
   
   # standalone training
   bash run_standalone_train.sh [PRETRAINED_CKPT]
   

   Note:

   1. To speed up data preprocessing, MindSpore provide a data format named MindRecord, hence the first step is to generate MindRecord files based on COCO2017 dataset before training. The process of converting raw COCO2017 dataset to MindRecord format may take about 4 hours.
   2. For distributed training, a hccl configuration file with JSON format needs to be created in advance.
   3. PRETRAINED_CKPT is a resnet50 checkpoint that trained over ImageNet2012.you can train it with resnet50 scripts in modelzoo, and use src/convert_checkpoint.py to get the pretrain checkpoint file.
   4. For large models like MaskRCNN, it's better to export an external environment variable export HCCL_CONNECT_TIMEOUT=600 to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.

4. Execute eval script.
   After training, you can start evaluation as follows:

   # Evaluation
   bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]
   

   Note:

   1. VALIDATION_JSON_FILE is a label json file for evaluation.

5. Execute inference script.
   After training, you can start inference as follows:

   # inference
   bash run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
   

   Note:

   1. AIR_PATH is a model file, exported by export script file on the Ascend910 environment.
   2. ANN_FILE_PATH is a annotation file for inference.

Run in docker

1. Build docker images

# build docker
docker build -t maskrcnn:20.1.0 . --build-arg FROM_IMAGE_NAME=ascend-mindspore-arm:20.1.0

2. Create a container layer over the created image and start it

# start docker
bash scripts/docker_start.sh maskrcnn:20.1.0 [DATA_DIR] [MODEL_DIR]

3. Train

# standalone training
bash run_standalone_train.sh [PRETRAINED_CKPT]

# distributed training
bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT]

4. Eval

# Evaluation
bash run_eval.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH]

5. Inference.

   # inference
   bash run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
   

Script Description

Script and Sample Code

.
└─MaskRcnn
  ├─README.md                             # README
  ├─ascend310_infer                       #application for 310 inference
  ├─scripts                               # shell script
    ├─run_standalone_train.sh             # training in standalone mode(1pcs)
    ├─run_distribute_train.sh             # training in parallel mode(8 pcs)
    ├─run_infer_310.sh                    #shell script for 310 inference
    └─run_eval.sh                         # evaluation
  ├─src
    ├─maskrcnn
      ├─__init__.py
      ├─anchor_generator.py               # generate base bounding box anchors
      ├─bbox_assign_sample.py             # filter positive and negative bbox for the first stage learning
      ├─bbox_assign_sample_stage2.py      # filter positive and negative bbox for the second stage learning
      ├─mask_rcnn_r50.py                  # main network architecture of maskrcnn
      ├─fpn_neck.py                       # fpn network
      ├─proposal_generator.py             # generate proposals based on feature map
      ├─rcnn_cls.py                       # rcnn bounding box regression branch
      ├─rcnn_mask.py                      # rcnn mask branch
      ├─resnet50.py                       # backbone network
      ├─roi_align.py                      # roi align network
      └─rpn.py                            # reagion proposal network
    ├─aipp.cfg                            #aipp config file
    ├─config.py                           # network configuration
    ├─convert_checkpoint.py               # convert resnet50 backbone checkpoint
    ├─dataset.py                          # dataset utils
    ├─lr_schedule.py                      # leanring rate geneatore
    ├─network_define.py                   # network define for maskrcnn
    └─util.py                             # routine operation
  ├─mindspore_hub_conf.py                 # mindspore hub interface
  ├─export.py                             #script to export AIR,MINDIR,ONNX model
  ├─eval.py                               # evaluation scripts
  ├─postprogress.py                       #post process for 310 inference
  └─train.py                              # training scripts

Script Parameters

Training Script Parameters

# distributed training
Usage: bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]

# standalone training
Usage: bash run_standalone_train.sh [PRETRAINED_MODEL]

Parameters Configuration

"img_width": 1280,          # width of the input images
"img_height": 768,          # height of the input images

# random threshold in data augmentation
"keep_ratio": True,
"flip_ratio": 0.5,
"expand_ratio": 1.0,

"max_instance_count": 128, # max number of bbox for each image
"mask_shape": (28, 28),    # shape of mask in rcnn_mask

# anchor
"feature_shapes": [(192, 320), (96, 160), (48, 80), (24, 40), (12, 20)], # shape of fpn feaure maps
"anchor_scales": [8],                                                    # area of base anchor
"anchor_ratios": [0.5, 1.0, 2.0],                                        # ratio between width of height of base anchors
"anchor_strides": [4, 8, 16, 32, 64],                                    # stride size of each feature map levels
"num_anchors": 3,                                                        # anchor number for each pixel

# resnet
"resnet_block": [3, 4, 6, 3],                                            # block number in each layer
"resnet_in_channels": [64, 256, 512, 1024],                              # in channel size for each layer
"resnet_out_channels": [256, 512, 1024, 2048],                           # out channel size for each layer

# fpn
"fpn_in_channels": [256, 512, 1024, 2048],                               # in channel size for each layer
"fpn_out_channels": 256,                                                 # out channel size for every layer
"fpn_num_outs": 5,                                                       # out feature map size

# rpn
"rpn_in_channels": 256,                                                  # in channel size
"rpn_feat_channels": 256,                                                # feature out channel size
"rpn_loss_cls_weight": 1.0,                                              # weight of bbox classification in rpn loss
"rpn_loss_reg_weight": 1.0,                                              # weight of bbox regression in rpn loss
"rpn_cls_out_channels": 1,                                               # classification out channel size
"rpn_target_means": [0., 0., 0., 0.],                                    # bounding box decode/encode means
"rpn_target_stds": [1.0, 1.0, 1.0, 1.0],                                 # bounding box decode/encode stds

# bbox_assign_sampler
"neg_iou_thr": 0.3,                                                      # negative sample threshold after IOU
"pos_iou_thr": 0.7,                                                      # positive sample threshold after IOU
"min_pos_iou": 0.3,                                                      # minimal positive sample threshold after IOU
"num_bboxes": 245520,                                                    # total bbox number
"num_gts": 128,                                                          # total ground truth number
"num_expected_neg": 256,                                                 # negative sample number
"num_expected_pos": 128,                                                 # positive sample number

# proposal
"activate_num_classes": 2,                                               # class number in rpn classification
"use_sigmoid_cls": True,                                                 # whethre use sigmoid as loss function in rpn classification

# roi_alignj
"roi_layer": dict(type='RoIAlign', out_size=7, mask_out_size=14, sample_num=2), # ROIAlign parameters
"roi_align_out_channels": 256,                                                  # ROIAlign out channels size
"roi_align_featmap_strides": [4, 8, 16, 32],                                    # stride size for different level of ROIAling feature map
"roi_align_finest_scale": 56,                                                   # finest scale ofr ROIAlign
"roi_sample_num": 640,                                                          # sample number in ROIAling layer

# bbox_assign_sampler_stage2                                                    # bbox assign sample for the second stage, parameter meaning is similar with bbox_assign_sampler
"neg_iou_thr_stage2": 0.5,
"pos_iou_thr_stage2": 0.5,
"min_pos_iou_stage2": 0.5,
"num_bboxes_stage2": 2000,
"num_expected_pos_stage2": 128,
"num_expected_neg_stage2": 512,
"num_expected_total_stage2": 512,

# rcnn                                                                          # rcnn parameter for the second stage, parameter meaning is similar with fpn
"rcnn_num_layers": 2,
"rcnn_in_channels": 256,
"rcnn_fc_out_channels": 1024,
"rcnn_mask_out_channels": 256,
"rcnn_loss_cls_weight": 1,
"rcnn_loss_reg_weight": 1,
"rcnn_loss_mask_fb_weight": 1,
"rcnn_target_means": [0., 0., 0., 0.],
"rcnn_target_stds": [0.1, 0.1, 0.2, 0.2],

# train proposal
"rpn_proposal_nms_across_levels": False,
"rpn_proposal_nms_pre": 2000,                                                  # proposal number before nms in rpn
"rpn_proposal_nms_post": 2000,                                                 # proposal number after nms in rpn
"rpn_proposal_max_num": 2000,                                                  # max proposal number in rpn
"rpn_proposal_nms_thr": 0.7,                                                   # nms threshold for nms in rpn
"rpn_proposal_min_bbox_size": 0,                                               # min size of box in rpn

# test proposal                                                                # part of parameters are similar with train proposal
"rpn_nms_across_levels": False,
"rpn_nms_pre": 1000,
"rpn_nms_post": 1000,
"rpn_max_num": 1000,
"rpn_nms_thr": 0.7,
"rpn_min_bbox_min_size": 0,
"test_score_thr": 0.05,                                                        # score threshold
"test_iou_thr": 0.5,                                                           # IOU threshold
"test_max_per_img": 100,                                                       # max number of instance
"test_batch_size": 2,                                                          # batch size

"rpn_head_use_sigmoid": True,                                                  # whether use sigmoid or not in rpn
"rpn_head_weight": 1.0,                                                        # rpn head weight in loss
"mask_thr_binary": 0.5,                                                        # mask threshold for in rcnn

# LR
"base_lr": 0.02,                                                               # base learning rate
"base_step": 58633,                                                            # bsae step in lr generator
"total_epoch": 13,                                                             # total epoch in lr generator
"warmup_step": 500,                                                            # warmp up step in lr generator
"warmup_ratio": 1/3.0,                                                         # warpm up ratio
"sgd_momentum": 0.9,                                                           # momentum in optimizer

# train
"batch_size": 2,
"loss_scale": 1,
"momentum": 0.91,
"weight_decay": 1e-4,
"pretrain_epoch_size": 0,                                                      # pretrained epoch size
"epoch_size": 12,                                                              # total epoch size
"save_checkpoint": True,                                                       # whether save checkpoint or not
"save_checkpoint_epochs": 1,                                                   # save checkpoint interval
"keep_checkpoint_max": 12,                                                     # max number of saved checkpoint
"save_checkpoint_path": "./",                                                  # path of checkpoint

"mindrecord_dir": "/home/maskrcnn/MindRecord_COCO2017_Train",                  # path of mindrecord
"coco_root": "/home/maskrcnn/",                                                # path of coco root dateset
"train_data_type": "train2017",                                                # name of train dataset
"val_data_type": "val2017",                                                    # name of evaluation dataset
"instance_set": "annotations/instances_{}.json",                               # name of annotation
"coco_classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
                 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
                 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
                 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
                 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
                 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
                 'kite', 'baseball bat', 'baseball glove', 'skateboard',
                 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
                 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
                 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
                 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
                 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
                 'refrigerator', 'book', 'clock', 'vase', 'scissors',
                 'teddy bear', 'hair drier', 'toothbrush'),
"num_classes": 81

Training Process

• Set options in config.py, including loss_scale, learning rate and network hyperparameters. Click here for more information about dataset.

Training

• Run run_standalone_train.sh for non-distributed training of MaskRCNN model.

# standalone training
bash run_standalone_train.sh [PRETRAINED_MODEL]

Distributed Training

• Run run_distribute_train.sh for distributed training of Mask model.

bash run_distribute_train.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL]

• Notes

1. hccl.json which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the hccl_tools.
2. As for PRETRAINED_MODEL，it should be a trained ResNet50 checkpoint. If not set, the model will be trained from the very beginning. If you need to load Ready-made pretrained MaskRcnn checkpoint, you may make changes to the train.py script as follows.

# Comment out the following code
#   load_path = args_opt.pre_trained
#    if load_path != "":
#        param_dict = load_checkpoint(load_path)
#        for item in list(param_dict.keys()):
#            if not item.startswith('backbone'):
#                param_dict.pop(item)
#        load_param_into_net(net, param_dict)

# Add the following codes after optimizer definition since the FasterRcnn checkpoint includes optimizer parameters：
    lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
                mstype.float32)
    opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
                   weight_decay=config.weight_decay, loss_scale=config.loss_scale)

    if load_path != "":
        param_dict = load_checkpoint(load_path)
        if config.pretrain_epoch_size == 0:
            for item in list(param_dict.keys()):
                if item in ("global_step", "learning_rate") or "rcnn.cls" in item or "rcnn.mask" in item:
                    param_dict.pop(item)
        load_param_into_net(net, param_dict)
        load_param_into_net(opt, param_dict)

3. This is processor cores binding operation regarding the device_num and total processor numbers. If you are not expect to do it, remove the operations taskset in scripts/run_distribute_train.sh

Training Result

Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". You can find checkpoint file together with result like the following in loss_rankid.log.

# distribute training result(8p)
epoch: 1 step: 7393 ,rpn_loss: 0.05716, rcnn_loss: 0.81152, rpn_cls_loss: 0.04828, rpn_reg_loss: 0.00889, rcnn_cls_loss: 0.28784, rcnn_reg_loss: 0.17590, rcnn_mask_loss: 0.34790, total_loss: 0.86868
epoch: 2 step: 7393 ,rpn_loss: 0.00434, rcnn_loss: 0.36572, rpn_cls_loss: 0.00339, rpn_reg_loss: 0.00095, rcnn_cls_loss: 0.08240, rcnn_reg_loss: 0.05554, rcnn_mask_loss: 0.22778, total_loss: 0.37006
epoch: 3 step: 7393 ,rpn_loss: 0.00996, rcnn_loss: 0.83789, rpn_cls_loss: 0.00701, rpn_reg_loss: 0.00294, rcnn_cls_loss: 0.39478, rcnn_reg_loss: 0.14917, rcnn_mask_loss: 0.29370, total_loss: 0.84785
...
epoch: 10 step: 7393 ,rpn_loss: 0.00667, rcnn_loss: 0.65625, rpn_cls_loss: 0.00536, rpn_reg_loss: 0.00131, rcnn_cls_loss: 0.17590, rcnn_reg_loss: 0.16199, rcnn_mask_loss: 0.31812, total_loss: 0.66292
epoch: 11 step: 7393 ,rpn_loss: 0.02003, rcnn_loss: 0.52051, rpn_cls_loss: 0.01761, rpn_reg_loss: 0.00241, rcnn_cls_loss: 0.16028, rcnn_reg_loss: 0.08411, rcnn_mask_loss: 0.27588, total_loss: 0.54054
epoch: 12 step: 7393 ,rpn_loss: 0.00547, rcnn_loss: 0.39258, rpn_cls_loss: 0.00285, rpn_reg_loss: 0.00262, rcnn_cls_loss: 0.08002, rcnn_reg_loss: 0.04990, rcnn_mask_loss: 0.26245, total_loss: 0.39804

Evaluation Process

Evaluation

• Run run_eval.sh for evaluation.

# infer
bash run_eval.sh [VALIDATION_ANN_FILE_JSON] [CHECKPOINT_PATH]

As for the COCO2017 dataset, VALIDATION_ANN_FILE_JSON is refer to the annotations/instances_val2017.json in the dataset directory.
checkpoint can be produced and saved in training process, whose folder name begins with "train/checkpoint" or "train_parallel*/checkpoint".

Images size in dataset should be equal to the annotation size in VALIDATION_ANN_FILE_JSON, otherwise the evaluation result cannot be displayed properly.

Evaluation result

Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the following in log.

Evaluate annotation type *bbox*
Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.378
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.602
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.242
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.497
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.524
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.363
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.567
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.647

Evaluate annotation type *segm*
Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.557
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.169
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.365
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.480
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.284
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.433
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.451
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.490
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.586

Model Export

python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]

EXPORT_FORMAT should be in ["AIR", "ONNX", "MINDIR"]

Inference Process

Usage

Before performing inference, the air file must bu exported by export script on the 910 environment.
Current batch_ Size can only be set to 1. The inference process needs about 600G hard disk space to save the reasoning results.

# Ascend310 inference
sh run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]

result

Inference result is saved in current path, you can find result like this in acc.log file.

Evaluate annotation type *bbox*
Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.3368
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.589
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.394
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.218
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.411
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.476
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.305
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.323
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657

Evaluate annotation type *segm*
Accumulating evaluation results...
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.323
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.544
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.336
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.147
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.353
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.479
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.278
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.422
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.439
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.248
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.478
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.594

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Model Version	V1
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G
uploaded Date	08/01/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	COCO2017
Training Parameters	epoch=12, batch_size = 2
Optimizer	SGD
Loss Function	Softmax Cross Entropy, Sigmoid Cross Entropy, SmoothL1Loss
Output	Probability
Loss	0.39804
Speed	1pc: 193 ms/step; 8pcs: 207 ms/step
Total time	1pc: 46 hours; 8pcs: 5.38 hours
Parameters (M)	84.8
Checkpoint for Fine tuning	85M(.ckpt file)
Model for inference	571M(.air file)
Scripts	maskrcnn script

Inference Performance

Parameters	Ascend
Model Version	V1
Resource	Ascend 910
Uploaded Date	08/01/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	COCO2017
batch_size	2
outputs	mAP
Accuracy	IoU=0.50:0.95 (BoundingBox 37.0%, Mask 33.5)
Model for inference	170M (.ckpt file)

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py for weight initialization.

ModelZoo Homepage

Please check the official homepage.