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guan_wen_cong
baf54dc699
|
1 year ago | |
|---|---|---|
| ascend310_infer | 1 year ago | |
| scripts | 1 year ago | |
| src | 1 year ago | |
| Dockerfile | 1 year ago | |
| README.md | 1 year ago | |
| README_CN.md | 1 year ago | |
| default_config.yaml | 1 year ago | |
| default_config_101.yaml | 1 year ago | |
| default_config_152.yaml | 1 year ago | |
| eval.py | 1 year ago | |
| export.py | 1 year ago | |
| mindspore_hub_conf.py | 1 year ago | |
| postprocess.py | 1 year ago | |
| requirements.txt | 1 year ago | |
| train.py | 1 year ago | |
README.md
Contents
- FasterRcnn Description
- Model Architecture
- Dataset
- Environment Requirements
- Quick Start
- Run in docker
- Script Description
- Model Description
- ModelZoo Homepage
FasterRcnn Description
Before FasterRcnn, the target detection networks rely on the region proposal algorithm to assume the location of targets, such as SPPnet and Fast R-CNN. Progress has reduced the running time of these detection networks, but it also reveals that the calculation of the region proposal is a bottleneck.
FasterRcnn proposed that convolution feature maps based on region detectors (such as Fast R-CNN) can also be used to generate region proposals. At the top of these convolution features, a Region Proposal Network (RPN) is constructed by adding some additional convolution layers (which share the convolution characteristics of the entire image with the detection network, thus making it possible to make regions almost costlessProposal), outputting both region bounds and objectness score for each location.Therefore, RPN is a full convolutional network (FCN), which can be trained end-to-end, generate high-quality region proposals, and then fed into Fast R-CNN for detection.
Paper: Ren S , He K , Girshick R , et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(6).
Model Architecture
FasterRcnn is a two-stage target detection network,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 FastRcnn into a network by sharing the convolution features.
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.
Dataset used: COCO2017
- 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/GPU）
- Prepare hardware environment with Ascend processor.
-
Docker base image
-
Install MindSpore.
-
Download the dataset COCO2017.
-
We use COCO2017 as training dataset in this example by default, and you can also use your own datasets.
-
If coco dataset is used. Select dataset to coco when run script.
Install Cython and pycocotool, and you can also install mmcv to process data.pip install Cython pip install pycocotools pip install mmcv==0.2.14And change the COCO_ROOT and other settings you need in
default_config.yaml、default_config_101.yaml or default_config_152.yaml. The directory structure is as follows:. └─cocodataset ├─annotations ├─instance_train2017.json └─instance_val2017.json ├─val2017 └─train2017 -
If your own dataset is used. Select dataset to other when run script.
Organize the dataset information into a TXT file, each row in the file is as follows:train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class information of 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 inANNO_PATH(the TXT file path),IMAGE_DIRandANNO_PATHare setting indefault_config_50.yaml、default_config_101.yaml or default_config_152.yaml.
-
Quick Start
After installing MindSpore via the official website, you can start training and evaluation as follows:
Note:
- the first run will generate the mindeocrd file, which will take a long time.
- pretrained model 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 model.
- BACKBONE_MODEL is a checkpoint file trained with resnet50 scripts in modelzoo.PRETRAINED_MODEL is a checkpoint file after convert.VALIDATION_JSON_FILE is label file. CHECKPOINT_PATH is a checkpoint file after trained.
Run on Ascend
# convert checkpoint
python convert_checkpoint.py --ckpt_file=[BACKBONE_MODEL]
# standalone training
bash run_standalone_train_ascend.sh [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# distributed training
bash run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# eval
bash run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# inference
bash run_infer_310.sh [AIR_PATH] [DATA_PATH] [ANN_FILE_PATH]
Run on GPU
# convert checkpoint
python convert_checkpoint.py --ckpt_file=[BACKBONE_MODEL]
# standalone training
bash run_standalone_train_gpu.sh [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# distributed training
bash run_distribute_train_gpu.sh [DEVICE_NUM] [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# eval
bash run_eval_gpu.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
Run in docker
- Build docker images
# build docker
docker build -t fasterrcnn:20.1.0 . --build-arg FROM_IMAGE_NAME=ascend-mindspore-arm:20.1.0
- Create a container layer over the created image and start it
# start docker
bash scripts/docker_start.sh fasterrcnn:20.1.0 [DATA_DIR] [MODEL_DIR]
- Train
# standalone training
bash run_standalone_train_ascend.sh [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# distributed training
bash run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
- Eval
# eval
bash run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
- Inference
# inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
-
Running on ModelArts
# Train 8p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "distribute=True" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set "epoch_size: 20" on default_config.yaml file. # (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "distribute=True" on the website UI interface. # Add "dataset_path=/cache/data" on the website UI interface. # Add "epoch_size: 20" on the website UI interface. # (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Prepare model code # (3) Upload or copy your pretrained model to S3 bucket if you want to finetune. # (4) Perform a or b. (suggested option a) # a. First, zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/faster_rcnn" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Train 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set "epoch_size: 20" on default_config.yaml file. # (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "dataset_path='/cache/data'" on the website UI interface. # Add "epoch_size: 20" on the website UI interface. # (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Prepare model code # (3) Upload or copy your pretrained model to S3 bucket if you want to finetune. # (4) Perform a or b. (suggested option a) # a. zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/faster_rcnn" on the website UI interface. # (6) Set the startup file to "train.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. # # Eval 1p with Ascend # (1) Perform a or b. # a. Set "enable_modelarts=True" on default_config.yaml file. # Set "checkpoint_url='s3://dir_to_your_trained_model/'" on base_config.yaml file. # Set "checkpoint='./faster_rcnn/faster_rcnn_trained.ckpt'" on default_config.yaml file. # Set "dataset_path='/cache/data'" on default_config.yaml file. # Set other parameters on default_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "checkpoint_url='s3://dir_to_your_trained_model/'" on the website UI interface. # Add "checkpoint='./faster_rcnn/faster_rcnn_trained.ckpt'" on the website UI interface. # Add "dataset_path='/cache/data'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Prepare model code # (3) Upload or copy your trained model to S3 bucket. # (4) Perform a or b. (suggested option a) # a. First, zip MindRecord dataset to one zip file. # Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.) # b. Upload the original dataset to S3 bucket. # (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.) # (5) Set the code directory to "/path/faster_rcnn" on the website UI interface. # (6) Set the startup file to "eval.py" on the website UI interface. # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (8) Create your job. -
Export on ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start evaluating as follows)
-
Export s8 multiscale and flip with voc val dataset on modelarts, evaluating steps are as follows:
# (1) Perform a or b. # a. Set "enable_modelarts=True" on base_config.yaml file. # Set "file_name='faster_rcnn'" on base_config.yaml file. # Set "file_format='AIR'" on base_config.yaml file. # Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file. # Set "ckpt_file='/cache/checkpoint_path/model.ckpt'" on base_config.yaml file. # Set other parameters on base_config.yaml file you need. # b. Add "enable_modelarts=True" on the website UI interface. # Add "file_name='faster_rcnn'" on the website UI interface. # Add "file_format='AIR'" on the website UI interface. # Add "checkpoint_url='/The path of checkpoint in S3/'" on the website UI interface. # Add "ckpt_file='/cache/checkpoint_path/model.ckpt'" on the website UI interface. # Add other parameters on the website UI interface. # (2) Upload or copy your trained model to S3 bucket. # (3) Set the code directory to "/path/faster_rcnn" on the website UI interface. # (4) Set the startup file to "export.py" on the website UI interface. # (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface. # (6) Create your job.
Script Description
Script and Sample Code
.
└─faster_rcnn
├─README.md // descriptions about fasterrcnn
├─ascend310_infer //application for 310 inference
├─scripts
├─run_standalone_train_ascend.sh // shell script for standalone on ascend
├─run_standalone_train_gpu.sh // shell script for standalone on GPU
├─run_distribute_train_ascend.sh // shell script for distributed on ascend
├─run_distribute_train_gpu.sh // shell script for distributed on GPU
├─run_infer_310.sh // shell script for 310 inference
└─run_eval_ascend.sh // shell script for eval on ascend
└─run_eval_gpu.sh // shell script for eval on GPU
├─src
├─FasterRcnn
├─__init__.py // init file
├─anchor_generator.py // anchor generator
├─bbox_assign_sample.py // first stage sampler
├─bbox_assign_sample_stage2.py // second stage sampler
├─faster_rcnn_resnet.py // fasterrcnn network
├─faster_rcnn_resnet50v1.py //fasterrcnn network for ResNet50v1.0
├─fpn_neck.py //feature pyramid network
├─proposal_generator.py // proposal generator
├─rcnn.py // rcnn network
├─resnet.py // backbone network
├─resnet50v1.py // backbone network for ResNet50v1.0
├─roi_align.py // roi align network
└─rpn.py // region proposal network
├─dataset.py // create dataset and process dataset
├─lr_schedule.py // learning ratio generator
├─network_define.py // network define for fasterrcnn
├─util.py // routine operation
└─model_utils
├─config.py // Processing configuration parameters
├─device_adapter.py // Get cloud ID
├─local_adapter.py // Get local ID
└─moxing_adapter.py // Parameter processing
├─default_config.yaml // config for ResNet50
├─default_config_101.yaml // config for ResNet101
├─default_config_152.yaml // config for ResNet152
├─export.py // script to export AIR,MINDIR,ONNX model
├─eval.py //eval scripts
├─postprogress.py // post process for 310 inference
└─train.py // train scripts
`BACKBONE` should be in ["resnet_v1.5_50", "resnet_v1_101", "resnet_v1_152", "resnet_v1_50"]
if backbone in ("resnet_v1.5_50", "resnet_v1_101", "resnet_v1_152"):
from src.FasterRcnn.faster_rcnn_resnet import Faster_Rcnn_Resnet
"resnet_v1.5_50" -> "./default_config.yaml"
"resnet_v1_101" -> "./default_config_101.yaml"
"resnet_v1_152" -> "./default_config_152.yaml"
elif backbone == "resnet_v1_50":
from src.FasterRcnn.faster_rcnn_resnet50v1 import Faster_Rcnn_Resnet
"resnet_v1_50" -> "./default_config.yaml"
Training Process
Usage
on Ascend
# standalone training on ascend
bash run_standalone_train_ascend.sh [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# distributed training on ascend
bash run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
on GPU
# standalone training on gpu
bash run_standalone_train_gpu.sh [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
# distributed training on gpu
bash run_distribute_train_gpu.sh [DEVICE_NUM] [PRETRAINED_MODEL] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
Notes:
- Rank_table.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.
- As for PRETRAINED_MODEL，it should be a trained ResNet50 checkpoint. If you need to load Ready-made pretrained FasterRcnn 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), mstype.float32)
opt = SGD(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)
for item in list(param_dict.keys()):
if item in ("global_step", "learning_rate") or "rcnn.reg_scores" in item or "rcnn.cls_scores" in item:
param_dict.pop(item)
load_param_into_net(opt, param_dict)
load_param_into_net(net, param_dict)
- The original dataset path needs to be in the default_config_50.yaml、default_config_101.yaml、default_config_152.yaml,you can select "coco_root" or "image_dir".
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: 0.12054, rcnn_loss: 0.40601, rpn_cls_loss: 0.04025, rpn_reg_loss: 0.08032, rcnn_cls_loss: 0.25854, rcnn_reg_loss: 0.14746, total_loss: 0.52655
epoch: 2 step: 7393, rpn_loss: 0.06561, rcnn_loss: 0.50293, rpn_cls_loss: 0.02587, rpn_reg_loss: 0.03967, rcnn_cls_loss: 0.35669, rcnn_reg_loss: 0.14624, total_loss: 0.56854
epoch: 3 step: 7393, rpn_loss: 0.06940, rcnn_loss: 0.49658, rpn_cls_loss: 0.03769, rpn_reg_loss: 0.03165, rcnn_cls_loss: 0.36353, rcnn_reg_loss: 0.13318, total_loss: 0.56598
...
epoch: 10 step: 7393, rpn_loss: 0.03555, rcnn_loss: 0.32666, rpn_cls_loss: 0.00697, rpn_reg_loss: 0.02859, rcnn_cls_loss: 0.16125, rcnn_reg_loss: 0.16541, total_loss: 0.36221
epoch: 11 step: 7393, rpn_loss: 0.19849, rcnn_loss: 0.47827, rpn_cls_loss: 0.11639, rpn_reg_loss: 0.08209, rcnn_cls_loss: 0.29712, rcnn_reg_loss: 0.18115, total_loss: 0.67676
epoch: 12 step: 7393, rpn_loss: 0.00691, rcnn_loss: 0.10168, rpn_cls_loss: 0.00529, rpn_reg_loss: 0.00162, rcnn_cls_loss: 0.05426, rcnn_reg_loss: 0.04745, total_loss: 0.10859
Evaluation Process
Usage
on Ascend
# eval on ascend
bash run_eval_ascend.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
on GPU
# eval on GPU
bash run_eval_gpu.sh [VALIDATION_JSON_FILE] [CHECKPOINT_PATH] [BACKBONE] [COCO_ROOT] [MINDRECORD_DIR](option)
checkpoint can be produced in training process.
Images size in dataset should be equal to the annotation size in VALIDATION_JSON_FILE, otherwise the evaluation result cannot be displayed properly.
Result
Eval result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the following in log.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.360
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.586
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.385
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.229
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.402
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.441
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.299
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.487
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.346
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 ] = 0.631
Model Export
python export.py --config_path [CONFIG_PATH] --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT] --backbone [BACKBONE]
EXPORT_FORMAT should be in ["AIR", "MINDIR"]
BACKBONE should be in ["resnet_v1.5_50", "resnet_v1_101", "resnet_v1_152", "resnet_v1_50"]
Inference Process
Usage
Before performing inference, the model file must be exported by export script on the Ascend910 environment.
The following example only supports mindir inference with batch_size=1.
# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
result
Inference result is saved in current path, you can find result like this in acc.log file.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.349
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.570
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.369
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.435
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.295
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.476
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.503
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.330
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.622
Model Description
Performance
Evaluation Performance
| Parameters | Ascend | GPU |
|---|---|---|
| Model Version | V1 | V1 |
| Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8 | V100-PCIE 32G |
| uploaded Date | 08/31/2020 (month/day/year) | 02/10/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.2.0 |
| Dataset | COCO2017 | COCO2017 |
| Training Parameters | epoch=12, batch_size=2 | epoch=12, batch_size=2 |
| Optimizer | SGD | SGD |
| Loss Function | Softmax Cross Entropy ,Sigmoid Cross Entropy,SmoothL1Loss | Softmax Cross Entropy ,Sigmoid Cross Entropy,SmoothL1Loss |
| Speed | 1pc: 190 ms/step; 8pcs: 200 ms/step | 1pc: 320 ms/step; 8pcs: 335 ms/step |
| Total time | 1pc: 37.17 hours; 8pcs: 4.89 hours | 1pc: 63.09 hours; 8pcs: 8.25 hours |
| Parameters (M) | 250 | 250 |
| Scripts | fasterrcnn script | fasterrcnn script |
Inference Performance
| Parameters | Ascend | GPU |
|---|---|---|
| Model Version | V1 | V1 |
| Resource | Ascend 910; OS Euler2.8 | GPU |
| Uploaded Date | 08/31/2020 (month/day/year) | 02/10/2021 (month/day/year) |
| MindSpore Version | 1.0.0 | 1.2.0 |
| Dataset | COCO2017 | COCO2017 |
| batch_size | 2 | 2 |
| outputs | mAP | mAP |
| Accuracy | IoU=0.50: 58.6% | IoU=0.50: 59.1% |
| Model for inference | 250M (.ckpt file) | 250M (.ckpt file) |
ModelZoo Homepage
Please check the official homepage.
Faster R-CNN是一个两阶段目标检测网络，该网络采用RPN，可以与检测网络共享整个图像的卷积特征，可以几乎无代价地进行区域候选计算。整个网络通过共享卷积特征，进一步将RPN和Fast R-CNN合并为一个网络。在Faster R-CNN之前，目标检测网络依靠区域候选算法来假设目标的位置，如SPPNet、Fast R-CNN等。研究结果表明，这些检测网络的运行时间缩短了，但区域方案的计算仍是瓶颈。 Faster R-CNN提出，基于区域检测器（如Fast R-CNN）的卷积特征映射也可以用于生成区域候选。在这些卷积特征的顶部构建区域候选网络（RPN）需要添加一些额外的卷积层（与检测网络共享整个图像的卷积特征，可以几乎无代价地进行区域候选），同时输出每个位置的区域边界和客观性得分。因此，RPN是一个全卷积网络，可以端到端训练，生成高质量的区域候选，然后送入Fast R-CNN检测。
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