Clean up PaddleCV code in release/1.7 (#4342)

* remove yolov3, ssd, rcnn and PaddleDetection.
* remove ICNet, deeplabv3+.
* remove Research
* remove empty folder.

PaddleCV/PaddleDetection/.gitignore

-# Virtualenv
-/.venv/
-/venv/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-
-# C extensions
-*.so
-
-# json file
-*.json
-
-# Distribution / packaging
-/bin/
-/build/
-/develop-eggs/
-/dist/
-/eggs/
-/lib/
-/lib64/
-/output/
-/parts/
-/sdist/
-/var/
-/*.egg-info/
-/.installed.cfg
-/*.egg
-/.eggs
-
-# AUTHORS and ChangeLog will be generated while packaging
-/AUTHORS
-/ChangeLog
-
-# BCloud / BuildSubmitter
-/build_submitter.*
-/logger_client_log
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-.tox/
-.coverage
-.cache
-.pytest_cache
-nosetests.xml
-coverage.xml
-
-# Translations
-*.mo
-
-# Sphinx documentation
-/docs/_build/
-
-*.json
-
-
-dataset/coco/annotations
-dataset/coco/train2017
-dataset/coco/val2017
-dataset/voc/VOCdevkit

PaddleCV/PaddleDetection/.style.yapf

-[style]
-based_on_style = pep8
-column_limit = 80

PaddleCV/PaddleDetection/README.md

-# **This project has been deprecated, please visit https://github.com/PaddlePaddle/PaddleDetection**</font> <br /> 
-
-
-
-English | [简体中文](README_cn.md)
-
-# PaddleDetection
-
-The goal of PaddleDetection is to provide easy access to a wide range of object
-detection models in both industry and research settings. We design
-PaddleDetection to be not only performant, production-ready but also highly
-flexible, catering to research needs.
-
-
-<div align="center">
-  <img src="demo/output/000000570688.jpg" />
-</div>
-
-
-## Introduction
-
-Features:
-
-- Production Ready:
-
-  Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
-highly efficient inference engine, enables easy deployment in server environments.
-
-- Highly Flexible:
-
-  Components are designed to be modular. Model architectures, as well as data
-preprocess pipelines, can be easily customized with simple configuration
-changes.
-
-- Performance Optimized:
-
-  With the help of the underlying PaddlePaddle framework, faster training and
-reduced GPU memory footprint is achieved. Notably, YOLOv3 training is
-much faster compared to other frameworks. Another example is Mask-RCNN
-(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during
-multi-GPU training.
-
-Supported Architectures:
-
-|                     | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG  |
-| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: | :-----: | :--: |
-| Faster R-CNN        |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |    ✗    |  ✗   |
-| Faster R-CNN + FPN  |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |    ✗    |  ✗   |
-| Mask R-CNN          |   ✓    |                             ✓ |     x      |   ✓   |     ✗     |    ✗    |  ✗   |
-| Mask R-CNN + FPN    |   ✓    |                             ✓ |     ✓      |   ✓   |     ✗     |    ✗    |  ✗   |
-| Cascade Faster-RCNN |   ✓    |                             ✓ |     ✓      |   ✗   |     ✗     |    ✗    |  ✗   |
-| Cascade Mask-RCNN   |   ✓    |                             ✗ |     ✗      |   ✓   |     ✗     |    ✗    |  ✗   |
-| RetinaNet           |   ✓    |                             ✗ |     ✗      |   ✗   |     ✗     |    ✗    |  ✗   |
-| YOLOv3              |   ✓    |                             ✗ |     ✗      |   ✗   |     ✓     |    ✓    |  ✗   |
-| SSD                 |   ✗    |                             ✗ |     ✗      |   ✗   |     ✓     |    ✗    |  ✓   |
-
-<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
-
-Advanced Features:
-
-- [x] **Synchronized Batch Norm**: currently used by YOLOv3.
-- [x] **Group Norm**
-- [x] **Modulated Deformable Convolution**
-- [x] **Deformable PSRoI Pooling**
-
-**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
-
-## Get Started
-
-- [Installation guide](docs/INSTALL.md)
-- [Quick start on small dataset](docs/QUICK_STARTED.md)
-- [Guide to traing, evaluate and arguments description](docs/GETTING_STARTED.md)
-- [Guide to preprocess pipeline and custom dataset](docs/DATA.md)
-- [Introduction to the configuration workflow](docs/CONFIG.md)
-- [Examples for detailed configuration explanation](docs/config_example/)
-- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
-- [Transfer learning document](docs/TRANSFER_LEARNING.md)
-
-## Model Zoo
-
-- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
-- [Face detection models](configs/face_detection/README.md)
-- [Pretrained models for pedestrian  and vehicle detection](contrib/README.md)
-
-## Model compression
-
-- [ Quantification aware training example](slim/quantization)
-- [ Pruning compression example](slim/prune)
-
-## Depoly
-
-- [Export model for inference depolyment](docs/EXPORT_MODEL.md)
-- [C++ inference depolyment](inference/README.md)
-
-## Benchmark
-
-- [Inference benchmark](docs/BENCHMARK_INFER_cn.md)
-
-
-## Updates
-
-#### 10/2019
-
-- Face detection models included: BlazeFace, Faceboxes.
-- Enrich COCO models,  box mAP up to 51.9%.
-- Add CACacascade RCNN, one of the best single model of Objects365 2019 challenge Full Track champion.
-- Add pretrained models for pedestrian and vehicle detection.
-- Support mixed-precision training.
-- Add C++ inference depolyment.
-- Add model compression examples.
-
-#### 2/9/2019
-
-- Add retrained models for GroupNorm.
-
-- Add Cascade-Mask-RCNN+FPN.
-
-#### 5/8/2019
-
-- Add a series of models ralated modulated Deformable Convolution.
-
-#### 7/29/2019
-
-- Update Chinese docs for PaddleDetection
-- Fix bug in R-CNN models when train and test at the same time
-- Add ResNext101-vd + Mask R-CNN + FPN models
-- Add YOLOv3 on VOC models
-
-#### 7/3/2019
-
-- Initial release of PaddleDetection and detection model zoo
-- Models included: Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
-  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, and SSD.
-
-
-## Contributing
-
-Contributions are highly welcomed and we would really appreciate your feedback!!

PaddleCV/PaddleDetection/README_cn.md

-# **该项目已被迁移到 https://github.com/PaddlePaddle/PaddleDetection**
-
-[English](README.md) | 简体中文
-
-# PaddleDetection
-
-PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型。不仅性能优越、易于部署，而且能够灵活的满足算法研究的需求。
-
-<div align="center">
-  <img src="demo/output/000000570688.jpg" />
-</div>
-
-
-## 简介
-
-特性：
-
-- 易部署:
-
-  PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。
-
-- 高灵活度：
-
-  PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。
-
-- 高性能：
-
-  基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。
-
-支持的模型结构：
-
-|                    | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet | VGG |
-|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|:---:|
-| Faster R-CNN       | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
-| Faster R-CNN + FPN | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
-| Mask R-CNN         | ✓      |                             ✓ | x          | ✓     | ✗         | ✗       | ✗   |
-| Mask R-CNN + FPN   | ✓      |                             ✓ | ✓          | ✓     | ✗         | ✗       | ✗   |
-| Cascade Faster-CNN | ✓      |                             ✓ | ✓          | ✗     | ✗         | ✗       | ✗  |
-| Cascade Mask-CNN   | ✓      |                             ✗ | ✗          | ✓     | ✗         | ✗       | ✗   |
-| RetinaNet          | ✓      |                             ✗ | ✓          | ✗     | ✗         | ✗       | ✗   |
-| YOLOv3             | ✓      |                             ✗ | ✗          | ✗     | ✓         | ✓       | ✗   |
-| SSD                | ✗      |                             ✗ | ✗          | ✗     | ✓         | ✗       | ✓   |
-
-<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型提供了较大的精度提高和较少的性能损失。
-
-扩展特性：
-
-- [x] **Synchronized Batch Norm**: 目前在YOLOv3中使用。
-- [x] **Group Norm**
-- [x] **Modulated Deformable Convolution**
-- [x] **Deformable PSRoI Pooling**
-
-**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
-
-
-## 使用教程
-
-- [安装说明](docs/INSTALL_cn.md)
-- [快速开始](docs/QUICK_STARTED_cn.md)
-- [训练、评估及参数说明](docs/GETTING_STARTED_cn.md)
-- [数据预处理及自定义数据集](docs/DATA_cn.md)
-- [配置模块设计和介绍](docs/CONFIG_cn.md)
-- [详细的配置信息和参数说明示例](docs/config_example/)
-- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
-- [迁移学习教程](docs/TRANSFER_LEARNING_cn.md)
-
-## 模型库
-
-- [模型库](docs/MODEL_ZOO_cn.md)
-- [人脸检测模型](configs/face_detection/README.md)
-- [行人检测和车辆检测预训练模型](contrib/README_cn.md)
-
-
-## 模型压缩
-- [量化训练压缩示例](slim/quantization)
-- [剪枝压缩示例](slim/prune)
-
-## 推理部署
-
-- [模型导出教程](docs/EXPORT_MODEL.md)
-- [C++推理部署](inference/README.md)
-
-## Benchmark
-
-- [推理Benchmark](docs/BENCHMARK_INFER_cn.md)
-
-
-
-## 版本更新
-
-### 10/2019
-
-- 增加人脸检测模型BlazeFace、Faceboxes。
-- 丰富基于COCO的模型，精度高达51.9%。
-- 增加Objects365 2019 Challenge上夺冠的最佳单模型之一CACascade-RCNN。
-- 增加行人检测和车辆检测预训练模型。
-- 支持FP16训练。
-- 增加跨平台的C++推理部署方案。
-- 增加模型压缩示例。
-
-
-### 2/9/2019
-- 增加GroupNorm模型。
-- 增加CascadeRCNN+Mask模型。
-
-#### 5/8/2019
-- 增加Modulated Deformable Convolution系列模型。
-
-#### 7/22/2019
-
-- 增加检测库中文文档
-- 修复R-CNN系列模型训练同时进行评估的问题
-- 新增ResNext101-vd + Mask R-CNN + FPN模型
-- 新增基于VOC数据集的YOLOv3模型
-
-#### 7/3/2019
-
-- 首次发布PaddleDetection检测库和检测模型库
-- 模型包括：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask
-  R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, 和SSD.
-
-## 如何贡献代码
-
-我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。

PaddleCV/PaddleDetection/configs/cascade_mask_rcnn_r50_fpn_1x.yml

-architecture: CascadeMaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-use_gpu: true
-max_iters: 180000
-snapshot_iter: 10000
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/cascade_mask_rcnn_r50_fpn_1x/model_final/
-num_classes: 81
-
-CascadeMaskRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-  mask_assigner: MaskAssigner
-  mask_head: MaskHead
-
-ResNet:
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: affine_channel
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  sampling_ratio: 2
-  box_resolution: 7
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  fg_fraction: 0.25
-  fg_thresh: [0.5, 0.6, 0.7]
-
-MaskAssigner:
-  resolution: 28
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead 
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/cascade_rcnn_r50_fpn_1x.yml

-architecture: CascadeRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-weights: output/cascade_rcnn_r50_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-CascadeRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  variant: b
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  fg_thresh: [0.5, 0.6, 0.7]
-  fg_fraction: 0.25
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/cascade_rcnn_r50_fpn_1x_ms_test.yml

-architecture: CascadeRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-weights: output/cascade_rcnn_r50_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-CascadeRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  variant: b
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  fg_thresh: [0.5, 0.6, 0.7]
-  fg_fraction: 0.25
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-MultiScaleTEST:
-  score_thresh: 0.05
-  nms_thresh: 0.5
-  detections_per_im: 100
-  enable_voting: true
-  vote_thresh: 0.9
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: true
-    mean:
-    - 0.485
-    - 0.456
-    - 0.406
-    std:
-    - 0.229
-    - 0.224
-    - 0.225
-  - !MultiscaleTestResize
-    origin_target_size: 800
-    origin_max_size: 1333
-    target_size:
-    - 400
-    - 500
-    - 600
-    - 700
-    - 900
-    - 1000
-    - 1100
-    - 1200
-    max_size: 2000
-    use_flip: true
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadMSTest
-    pad_to_stride: 32
-  num_scale: 18
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x.yml

-architecture: CascadeMaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-max_iters: 300000
-snapshot_iter: 10
-use_gpu: true
-log_iter: 20
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar 
-weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/
-metric: COCO
-num_classes: 81
-
-CascadeMaskRCNN:
-  backbone: SENet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-  mask_assigner: MaskAssigner
-  mask_head: MaskHead
-
-SENet:
-  depth: 152
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  group_width: 4
-  groups: 64
-  norm_type: bn
-  freeze_norm: True
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-  std_senet: True
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-  freeze_norm: False
-  norm_type: gn
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-  norm_type: gn
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  fg_fraction: 0.25
-  fg_thresh: [0.5, 0.6, 0.7]
-
-MaskAssigner:
-  resolution: 28
-
-CascadeBBoxHead:
-  head: CascadeXConvNormHead 
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeXConvNormHead:
-  norm_type: gn
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 280000]
-  - !LinearWarmup
-    start_factor: 0.01
-    steps: 2000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  sample_transforms: 
-  - !DecodeImage
-    to_rgb: False
-    with_mixup: False
-  - !RandomFlipImage
-    is_mask_flip: true
-    is_normalized: false
-    prob: 0.5
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801 
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0 
-    - 1.0 
-    - 1.0 
-  - !ResizeImage
-    interp: 1
-    target_size:
-    - 416
-    - 448
-    - 480
-    - 512
-    - 544
-    - 576
-    - 608
-    - 640
-    - 672
-    - 704 
-    - 736
-    - 768
-    - 800
-    - 832
-    - 864
-    - 896
-    - 928
-    - 960
-    - 992
-    - 1024
-    - 1056
-    - 1088
-    - 1120
-    - 1152
-    - 1184
-    - 1216
-    - 1248
-    - 1280
-    - 1312
-    - 1344
-    - 1376
-    - 1408
-    max_size: 1600
-    use_cv2: true
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 8
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  sample_transforms: 
-  - !DecodeImage
-    to_rgb: False
-    with_mixup: False
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801 
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0 
-    - 1.0 
-    - 1.0 
-  - !ResizeImage
-    interp: 1
-    target_size:
-    - 800
-    max_size: 1333
-    use_cv2: true
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: False
-    with_mixup: False
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0
-    - 1.0
-    - 1.0
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/cascade_mask_rcnn_dcnv2_se154_vd_fpn_gn_s1x_ms_test.yml

-architecture: CascadeMaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-max_iters: 300000
-snapshot_iter: 10000
-use_gpu: true
-log_iter: 20
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_caffe_pretrained.tar 
-weights: output/cascade_mask_rcnn_dcn_se154_vd_fpn_gn_s1x/model_final/
-metric: COCO
-num_classes: 81
-
-CascadeMaskRCNN:
-  backbone: SENet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-  mask_assigner: MaskAssigner
-  mask_head: MaskHead
-
-SENet:
-  depth: 152
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  group_width: 4
-  groups: 64
-  norm_type: bn
-  freeze_norm: True
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-  std_senet: True
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-  freeze_norm: False
-  norm_type: gn
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-  norm_type: gn
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  fg_fraction: 0.25
-  fg_thresh: [0.5, 0.6, 0.7]
-
-MaskAssigner:
-  resolution: 28
-
-CascadeBBoxHead:
-  head: CascadeXConvNormHead 
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeXConvNormHead:
-  norm_type: gn
-
-MultiScaleTEST:
-  score_thresh: 0.05
-  nms_thresh: 0.5
-  detections_per_im: 100
-  enable_voting: true
-  vote_thresh: 0.9
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 280000]
-  - !LinearWarmup
-    start_factor: 0.01
-    steps: 2000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  sample_transforms: 
-  - !DecodeImage
-    to_rgb: False
-    with_mixup: False
-  - !RandomFlipImage
-    is_mask_flip: true
-    is_normalized: false
-    prob: 0.5
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801 
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0 
-    - 1.0 
-    - 1.0 
-  - !ResizeImage
-    interp: 1
-    target_size:
-    - 416
-    - 448
-    - 480
-    - 512
-    - 544
-    - 576
-    - 608
-    - 640
-    - 672
-    - 704 
-    - 736
-    - 768
-    - 800
-    - 832
-    - 864
-    - 896
-    - 928
-    - 960
-    - 992
-    - 1024
-    - 1056
-    - 1088
-    - 1120
-    - 1152
-    - 1184
-    - 1216
-    - 1248
-    - 1280
-    - 1312
-    - 1344
-    - 1376
-    - 1408
-    max_size: 1600
-    use_cv2: true
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 8
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  sample_transforms: 
-  - !DecodeImage
-    to_rgb: False
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801 
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0 
-    - 1.0 
-    - 1.0 
-  - !MultiscaleTestResize
-    origin_target_size: 800
-    origin_max_size: 1333
-    target_size:
-    - 400
-    - 500
-    - 600
-    - 700
-    - 900
-    - 1000
-    - 1100
-    - 1200
-    max_size: 2000
-    use_flip: true
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadMSTest
-    pad_to_stride: 32
-  # num_scale = (len(target_size) + 1) * (1 + use_flip)
-  num_scale: 18
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: False
-  - !NormalizeImage
-    is_channel_first: false
-    is_scale: False
-    mean:
-    - 102.9801
-    - 115.9465
-    - 122.7717
-    std:
-    - 1.0
-    - 1.0
-    - 1.0
-  - !Permute
-    channel_first: true
-    to_bgr: false
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/cascade_rcnn_dcn_r101_vd_fpn_1x.yml

-architecture: CascadeRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
-weights: output/cascade_rcnn_dcn_r101_vd_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-CascadeRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-
-ResNet:
-  norm_type: bn
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  fg_thresh: [0.5, 0.6, 0.7]
-  fg_fraction: 0.25
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/cascade_rcnn_dcn_r50_fpn_1x.yml

-architecture: CascadeRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-weights: output/cascade_rcnn_dcn_r50_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-CascadeRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-
-ResNet:
-  norm_type: bn
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  variant: b
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  fg_thresh: [0.5, 0.6, 0.7]
-  fg_fraction: 0.25
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml

-architecture: CascadeRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
-weights: output/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-CascadeRCNN:
-  backbone: ResNeXt
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: CascadeBBoxHead
-  bbox_assigner: CascadeBBoxAssigner
-
-ResNeXt:
-  norm_type: bn
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  group_width: 4                                                                                                                                          
-  groups: 64
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-CascadeBBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [10, 20, 30]
-  bg_thresh_lo: [0.0, 0.0, 0.0]
-  bg_thresh_hi: [0.5, 0.6, 0.7]
-  fg_thresh: [0.5, 0.6, 0.7]
-  fg_fraction: 0.25
-
-CascadeBBoxHead:
-  head: CascadeTwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-CascadeTwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
-weights: output/faster_rcnn_dcn_r101_vd_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/faster_rcnn_dcn_r50_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-use_gpu: true
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_dcn_r50_fpn_1x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 50
-  norm_type: bn
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_lo: 0.0
-  bg_thresh_hi: 0.5
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
-weights: output/faster_rcnn_dcn_r50_vd_fpn_2x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
-weights: output/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNeXt
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNeXt:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  group_width: 4
-  groups: 64
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-  shuffle: true
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-  shuffle: false

PaddleCV/PaddleDetection/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x.yml

-architecture: MaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar 
-weights: output/mask_rcnn_dcn_r101_vd_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-MaskRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  sampling_ratio: 2
-  box_resolution: 7
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-MaskAssigner:
-  resolution: 28
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/mask_rcnn_dcn_r50_fpn_1x.yml

-architecture: MaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-use_gpu: true
-max_iters: 180000
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/mask_rcnn_dcn_r50_fpn_1x/model_final/
-num_classes: 81
-
-MaskRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: bn
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  sampling_ratio: 2
-  box_resolution: 7
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-MaskAssigner:
-  resolution: 28
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x.yml

-architecture: MaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-use_gpu: true
-max_iters: 360000
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
-metric: COCO
-weights: output/mask_rcnn_dcn_r50_vd_fpn_2x/model_final/
-num_classes: 81
-
-MaskRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-MaskAssigner:
-  resolution: 28
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 320000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x.yml

-architecture: MaskRCNN
-train_feed: MaskRCNNTrainFeed
-eval_feed: MaskRCNNEvalFeed
-test_feed: MaskRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-log_iter: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar 
-weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-MaskRCNN:
-  backbone: ResNeXt
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNeXt:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  group_width: 4
-  groups: 64
-  norm_type: bn
-  variant: d
-  dcn_v2_stages: [3, 4, 5]
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    aspect_ratios: [0.5, 1.0, 2.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  sampling_ratio: 2
-  box_resolution: 7
-  mask_resolution: 14
-
-MaskHead:
-  dilation: 1
-  conv_dim: 256
-  num_convs: 4
-  resolution: 28
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-MaskAssigner:
-  resolution: 28
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-MaskRCNNTrainFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-MaskRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/face_detection/README.md

-English | [简体中文](README_cn.md)
-
-# FaceDetection
-The goal of FaceDetection is to provide efficient and high-speed face detection solutions,
-including cutting-edge and classic models.
-
-
-<div align="center">
-  <img src="../../demo/output/12_Group_Group_12_Group_Group_12_935.jpg" />
-</div>
-
-## Data Pipline
-We use the [WIDER FACE dataset](http://shuoyang1213.me/WIDERFACE/) to carry out the training
-and testing of the model, the official website gives detailed data introduction.
-- WIDER Face data source:  
-Loads `wider_face` type dataset with directory structures like this:
-
-  ```
-  dataset/wider_face/
-  ├── wider_face_split
-  │   ├── wider_face_train_bbx_gt.txt
-  │   ├── wider_face_val_bbx_gt.txt
-  ├── WIDER_train
-  │   ├── images
-  │   │   ├── 0--Parade
-  │   │   │   ├── 0_Parade_marchingband_1_100.jpg
-  │   │   │   ├── 0_Parade_marchingband_1_381.jpg
-  │   │   │   │   ...
-  │   │   ├── 10--People_Marching
-  │   │   │   ...
-  ├── WIDER_val
-  │   ├── images
-  │   │   ├── 0--Parade
-  │   │   │   ├── 0_Parade_marchingband_1_1004.jpg
-  │   │   │   ├── 0_Parade_marchingband_1_1045.jpg
-  │   │   │   │   ...
-  │   │   ├── 10--People_Marching
-  │   │   │   ...
-  ```
-
-- Download dataset manually:  
-To download the WIDER FACE dataset, run the following commands:
-```
-cd dataset/wider_face && ./download.sh
-```
-
-- Download dataset automatically:
-If a training session is started but the dataset is not setup properly
-(e.g, not found in dataset/wider_face), PaddleDetection can automatically
-download them from [WIDER FACE dataset](http://shuoyang1213.me/WIDERFACE/),
-the decompressed datasets will be cached in ~/.cache/paddle/dataset/ and can be discovered
-automatically subsequently.
-
-### Data Augmentation
-
-- **Data-anchor-sampling:** Randomly transform the scale of the image to a certain range of scales,
-greatly enhancing the scale change of the face. The specific operation is to obtain $v=\sqrt{width * height}$
-according to the randomly selected face height and width, and judge the value of `v` in which interval of
- `[16,32,64,128]`. Assuming `v=45` && `32<v<64`, and any value of `[16,32,64]` is selected with a probability
- of uniform distribution. If `64` is selected, the face's interval is selected in `[64 / 2, min(v * 2, 64 * 2)]`.
-
-- **Other methods:** Including `RandomDistort`,`ExpandImage`,`RandomInterpImage`,`RandomFlipImage` etc.
-Please refer to [DATA.md](../../docs/DATA.md#APIs) for details.
-
-
-##  Benchmark and Model Zoo
-Supported architectures is shown in the below table, please refer to
-[Algorithm Description](#Algorithm-Description) for details of the algorithm.
-
-|                          | Original | Lite <sup>[1](#lite)</sup> | NAS <sup>[2](#nas)</sup> |
-|:------------------------:|:--------:|:--------------------------:|:------------------------:|
-| [BlazeFace](#BlazeFace)  | ✓        |                          ✓ | ✓                        |
-| [FaceBoxes](#FaceBoxes)  | ✓        |                          ✓ | x                        |
-
-<a name="lite">[1]</a> `Lite` edition means reduces the number of network layers and channels.  
-<a name="nas">[2]</a> `NAS` edition means use `Neural Architecture Search` algorithm to
-optimized network structure.
-
-**Todo List:**
-- [ ] HamBox
-- [ ] Pyramidbox
-
-### Model Zoo
-
-#### mAP in WIDER FACE
-
-| Architecture | Type     | Size | Img/gpu | Lr schd | Easy Set  | Medium Set | Hard Set  | Download |
-|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|
-| BlazeFace    | Original | 640  |    8    | 32w     | **0.915** | **0.892**  | **0.797** | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) |
-| BlazeFace    | Lite     | 640  |    8    | 32w     | 0.909     | 0.885      | 0.781     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) |
-| BlazeFace    | NAS      | 640  |    8    | 32w     | 0.837     | 0.807      | 0.658     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
-| FaceBoxes    | Original | 640  |    8    | 32w     | 0.875     | 0.848      | 0.568     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) |
-| FaceBoxes    | Lite     | 640  |    8    | 32w     | 0.898     | 0.872      | 0.752     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) |
-
-**NOTES:**  
-- Get mAP in `Easy/Medium/Hard Set` by multi-scale evaluation in `tools/face_eval.py`.
-For details can refer to [Evaluation](#Evaluate-on-the-WIDER-FACE).
-- BlazeFace-Lite Training and Testing ues [blazeface.yml](../../configs/face_detection/blazeface.yml)
-configs file and set `lite_edition: true`.
-
-#### mAP in FDDB
-
-| Architecture | Type     | Size | DistROC | ContROC |
-|:------------:|:--------:|:----:|:-------:|:-------:|
-| BlazeFace    | Original | 640  | **0.992**   | **0.762**   |
-| BlazeFace    | Lite     | 640  | 0.990   | 0.756   |
-| BlazeFace    | NAS      | 640  | 0.981   | 0.741   |
-| FaceBoxes    | Original | 640  | 0.985   | 0.731   |
-| FaceBoxes    | Lite     | 640  | 0.987   | 0.741   |
-
-**NOTES:**  
-- Get mAP by multi-scale evaluation on the FDDB dataset.
-For details can refer to [Evaluation](#Evaluate-on-the-FDDB).
-
-#### Infer Time and Model Size comparison  
-
-| Architecture | Type     | Size | P4 (ms)   | CPU (ms) | ARM (ms)   | File size (MB) | Flops     |
-|:------------:|:--------:|:----:|:---------:|:--------:|:----------:|:--------------:|:---------:|
-| BlazeFace    | Original | 128  | -         | -        | -          | -              | -         |
-| BlazeFace    | Lite     | 128  | -         | -        | -          | -              | -         |
-| BlazeFace    | NAS      | 128  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Original | 128  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Lite     | 128  | -         | -        | -          | -              | -         |
-| BlazeFace    | Original | 320  | -         | -        | -          | -              | -         |
-| BlazeFace    | Lite     | 320  | -         | -        | -          | -              | -         |
-| BlazeFace    | NAS      | 320  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Original | 320  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Lite     | 320  | -         | -        | -          | -              | -         |
-| BlazeFace    | Original | 640  | -         | -        | -          | -              | -         |
-| BlazeFace    | Lite     | 640  | -         | -        | -          | -              | -         |
-| BlazeFace    | NAS      | 640  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Original | 640  | -         | -        | -          | -              | -         |
-| FaceBoxes    | Lite     | 640  | -         | -        | -          | -              | -         |
-
-
-**NOTES:**  
-- CPU: i5-7360U @ 2.30GHz. Single core and single thread.
-
-
-
-## Get Started
-`Training` and `Inference` please refer to [GETTING_STARTED.md](../../docs/GETTING_STARTED.md)
-- **NOTES:**     
-- `BlazeFace` and `FaceBoxes` is trained in 4 GPU with `batch_size=8` per gpu (total batch size as 32) 
-and trained 320000 iters.(If your GPU count is not 4, please refer to the rule of training parameters 
-in the table of [calculation rules](../../docs/GETTING_STARTED.md#faq))
-- Currently we do not support evaluation in training.
-
-### Evaluation
-```
-export CUDA_VISIBLE_DEVICES=0
-export PYTHONPATH=$PYTHONPATH:.
-python tools/face_eval.py -c configs/face_detection/blazeface.yml
-```
-- Optional arguments
-- `-d` or `--dataset_dir`: Dataset path, same as dataset_dir of configs. Such as: `-d dataset/wider_face`.
-- `-f` or `--output_eval`: Evaluation file directory, default is `output/pred`.
-- `-e` or `--eval_mode`: Evaluation mode, include `widerface` and `fddb`, default is `widerface`.
-- `--multi_scale`: If you add this action button in the command, it will select `multi_scale` evaluation. 
-Default is `False`, it will select `single-scale` evaluation.
-
-After the evaluation is completed, the test result in txt format will be generated in `output/pred`,
-and then mAP will be calculated according to different data sets. If you set `--eval_mode=widerface`,
-it will [Evaluate on the WIDER FACE](#Evaluate-on-the-WIDER-FACE).If you set `--eval_mode=fddb`,
-it will [Evaluate on the FDDB](#Evaluate-on-the-FDDB).
-
-#### Evaluate on the WIDER FACE
-- Download the official evaluation script to evaluate the AP metrics:
-```
-wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip
-unzip eval_tools.zip && rm -f eval_tools.zip
-```
-- Modify the result path and the name of the curve to be drawn in `eval_tools/wider_eval.m`:
-```
-# Modify the folder name where the result is stored.
-pred_dir = './pred';  
-# Modify the name of the curve to be drawn
-legend_name = 'Fluid-BlazeFace';
-```
-- `wider_eval.m` is the main execution program of the evaluation module. The run command is as follows:
-```
-matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;"
-```
-
-#### Evaluate on the FDDB
-[FDDB dataset](http://vis-www.cs.umass.edu/fddb/) details can refer to FDDB's official website.   
-- Download the official dataset and evaluation script to evaluate the ROC metrics:
-```
-#external link to the Faces in the Wild data set
-wget http://tamaraberg.com/faceDataset/originalPics.tar.gz
-#The annotations are split into ten folds. See README for details.
-wget http://vis-www.cs.umass.edu/fddb/FDDB-folds.tgz
-#information on directory structure and file formats
-wget http://vis-www.cs.umass.edu/fddb/README.txt
-```
-- Install OpenCV: Requires [OpenCV library](http://sourceforge.net/projects/opencvlibrary/)  
-If the utility 'pkg-config' is not available for your operating system,
-edit the Makefile to manually specify the OpenCV flags as following:
-```
-INCS = -I/usr/local/include/opencv
-LIBS = -L/usr/local/lib -lcxcore -lcv -lhighgui -lcvaux -lml
-```
-
-- Compile FDDB evaluation code: execute `make` in evaluation folder.
-
-- Generate full image path list and groundtruth in FDDB-folds. The run command is as follows:
-```
-cat `ls|grep -v"ellipse"` > filePath.txt` and `cat *ellipse* > fddb_annotFile.txt`
-```
-- Evaluation
-Finally evaluation command is:
-```
-./evaluate -a ./FDDB/FDDB-folds/fddb_annotFile.txt \
-           -d DETECTION_RESULT.txt -f 0 \
-           -i ./FDDB -l ./FDDB/FDDB-folds/filePath.txt \
-           -r ./OUTPUT_DIR -z .jpg
-```
-**NOTES:** The interpretation of the argument can be performed by `./evaluate --help`.
-
-## Algorithm Description
-
-### BlazeFace
-**Introduction:**  
-[BlazeFace](https://arxiv.org/abs/1907.05047) is Google Research published face detection model.
-It's lightweight but good performance, and tailored for mobile GPU inference. It runs at a speed
-of 200-1000+ FPS on flagship devices.
-
-**Particularity:**  
-- Anchor scheme stops at 8×8(input 128x128), 6 anchors per pixel at that resolution.
-- 5 single, and 6 double BlazeBlocks: 5×5 depthwise convs, same accuracy with fewer layers.
-- Replace the non-maximum suppression algorithm with a blending strategy that estimates the
-regression parameters of a bounding box as a weighted mean between the overlapping predictions.
-
-**Edition information:**
-- Original: Reference original paper reproduction.
-- Lite: Replace 5x5 conv with 3x3 conv, fewer network layers and conv channels.
-- NAS: use `Neural Architecture Search` algorithm to optimized network structure,
-less network layer and conv channel number than `Lite`.
-
-### FaceBoxes
-**Introduction:**     
-[FaceBoxes](https://arxiv.org/abs/1708.05234) which named A CPU Real-time Face Detector
-with High Accuracy is face detector proposed by Shifeng Zhang, with high performance on
-both speed and accuracy. This paper is published by IJCB(2017).
-
-**Particularity:**
-- Anchor scheme stops at 20x20, 10x10, 5x5, which network input size is 640x640,
-including 3, 1, 1 anchors per pixel at each resolution. The corresponding densities
-are 1, 2, 4(20x20), 4(10x10) and 4(5x5).
-- 2 convs with CReLU, 2 poolings, 3 inceptions and 2 convs with ReLU.
-- Use density prior box to improve detection accuracy.
-
-**Edition information:**
-- Original: Reference original paper reproduction.
-- Lite: 2 convs with CReLU, 1 pooling, 2 convs with ReLU, 3 inceptions and 2 convs with ReLU.
-Anchor scheme stops at 80x80 and 40x40, including 3, 1 anchors per pixel at each resolution.
-The corresponding densities are 1, 2, 4(80x80) and 4(40x40), using less conv channel number than lite.
-
-
-## Contributing
-Contributions are highly welcomed and we would really appreciate your feedback!!

PaddleCV/PaddleDetection/configs/face_detection/blazeface.yml

-architecture: BlazeFace
-max_iters: 320000
-train_feed: SSDTrainFeed
-eval_feed: SSDEvalFeed
-test_feed: SSDTestFeed
-pretrain_weights: 
-use_gpu: true
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-metric: WIDERFACE
-save_dir: output
-weights: output/blazeface/model_final/
-# 1(label_class) + 1(background)
-num_classes: 2
-
-BlazeFace:
-  backbone: BlazeNet
-  output_decoder:
-    keep_top_k: 750
-    nms_threshold: 0.3
-    nms_top_k: 5000
-    score_threshold: 0.01
-  min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]]
-  use_density_prior_box: false
-
-BlazeNet:
-  with_extra_blocks: true
-  lite_edition: false
-
-LearningRate:
-  base_lr: 0.001
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 300000]
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.0
-    type: RMSPropOptimizer
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-SSDTrainFeed:
-  batch_size: 8
-  use_process: True
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_train_bbx_gt.txt
-    image_dir: WIDER_train/images
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !RandomDistort
-    brightness_lower: 0.875
-    brightness_upper: 1.125
-    is_order: true
-  - !ExpandImage
-    max_ratio: 4
-    prob: 0.5
-  - !CropImageWithDataAchorSampling
-    anchor_sampler:
-    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
-    batch_sampler:
-    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    target_size: 640
-  - !RandomInterpImage
-    target_size: 640
-  - !RandomFlipImage
-    is_normalized: true
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDEvalFeed:
-  batch_size: 1
-  use_process: false
-  fields: ['image', 'im_id', 'gt_box']
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_val_bbx_gt.txt   
-    image_dir: WIDER_val/images
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDTestFeed:
-  batch_size: 1
-  use_process: false
-  dataset:
-    use_default_label: true
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]

PaddleCV/PaddleDetection/configs/face_detection/blazeface_nas.yml

-architecture: BlazeFace
-max_iters: 320000
-train_feed: SSDTrainFeed
-eval_feed: SSDEvalFeed
-test_feed: SSDTestFeed
-pretrain_weights: 
-use_gpu: true
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-metric: WIDERFACE
-save_dir: output
-weights: output/blazeface_nas/model_final/
-# 1(label_class) + 1(background)
-num_classes: 2
-
-BlazeFace:
-  backbone: BlazeNet
-  output_decoder:
-    keep_top_k: 750
-    nms_threshold: 0.3
-    nms_top_k: 5000
-    score_threshold: 0.01
-  min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]]
-  use_density_prior_box: false
-
-BlazeNet:
-  blaze_filters: [[12, 12], [12, 12, 2], [12, 12]]
-  double_blaze_filters: [[12, 16, 24, 2], [24, 12, 24], [24, 16, 72, 2], [72, 12, 72]]
-  with_extra_blocks: true
-  lite_edition: false
-
-LearningRate:
-  base_lr: 0.001
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 300000]
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.0
-    type: RMSPropOptimizer
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-SSDTrainFeed:
-  batch_size: 8
-  use_process: True
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_train_bbx_gt.txt
-    image_dir: WIDER_train/images
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !RandomDistort
-    brightness_lower: 0.875
-    brightness_upper: 1.125
-    is_order: true
-  - !ExpandImage
-    max_ratio: 4
-    prob: 0.5
-  - !CropImageWithDataAchorSampling
-    anchor_sampler:
-    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
-    batch_sampler:
-    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    target_size: 640
-  - !RandomInterpImage
-    target_size: 640
-  - !RandomFlipImage
-    is_normalized: true
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDEvalFeed:
-  batch_size: 1
-  use_process: false
-  fields: ['image', 'im_id', 'gt_box']
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_val_bbx_gt.txt
-    image_dir: WIDER_val/images
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDTestFeed:
-  batch_size: 1
-  use_process: false
-  dataset:
-    use_default_label: true
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]

PaddleCV/PaddleDetection/configs/face_detection/faceboxes.yml

-architecture: FaceBoxes
-train_feed: SSDTrainFeed
-eval_feed: SSDEvalFeed
-test_feed: SSDTestFeed
-pretrain_weights:
-use_gpu: true
-max_iters: 320000
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-metric: WIDERFACE
-save_dir: output
-weights: output/faceboxes/model_final/
-# 1(label_class) + 1(background)
-num_classes: 2
-
-FaceBoxes:
-  backbone: FaceBoxNet
-  densities: [[4, 2, 1], [1], [1]]
-  fixed_sizes: [[32., 64., 128.], [256.], [512.]]
-  output_decoder:
-    keep_top_k: 750
-    nms_threshold: 0.3
-    nms_top_k: 5000
-    score_threshold: 0.01
-
-FaceBoxNet:
-  with_extra_blocks: true
-  lite_edition: false
-
-LearningRate:
-  base_lr: 0.001
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 300000]
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.0
-    type: RMSPropOptimizer
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-SSDTrainFeed:
-  batch_size: 8
-  use_process: True
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_train_bbx_gt.txt
-    image_dir: WIDER_train/images
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !RandomDistort
-    brightness_lower: 0.875
-    brightness_upper: 1.125
-    is_order: true
-  - !ExpandImage
-    max_ratio: 4
-    prob: 0.5
-  - !CropImageWithDataAchorSampling
-    anchor_sampler:
-    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
-    batch_sampler:
-    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    target_size: 640
-  - !RandomInterpImage
-    target_size: 640
-  - !RandomFlipImage
-    is_normalized: true
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDEvalFeed:
-  batch_size: 1
-  use_process: false
-  fields: ['image', 'im_id', 'gt_box']
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_val_bbx_gt.txt
-    image_dir: WIDER_val/images
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDTestFeed:
-  batch_size: 1
-  use_process: false
-  dataset:
-    use_default_label: true
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]

PaddleCV/PaddleDetection/configs/face_detection/faceboxes_lite.yml

-architecture: FaceBoxes
-train_feed: SSDTrainFeed
-eval_feed: SSDEvalFeed
-test_feed: SSDTestFeed
-pretrain_weights:
-use_gpu: true
-max_iters: 320000
-snapshot_iter: 10000
-log_smooth_window: 20
-log_iter: 20
-metric: WIDERFACE
-save_dir: output
-weights: output/faceboxes_lite/model_final/
-# 1(label_class) + 1(background)
-num_classes: 2
-
-FaceBoxes:
-  backbone: FaceBoxNet
-  densities: [[2, 1, 1], [1, 1]]
-  fixed_sizes: [[16., 32., 64.], [96., 128.]]
-  output_decoder:
-    keep_top_k: 750
-    nms_threshold: 0.3
-    nms_top_k: 5000
-    score_threshold: 0.01
-
-FaceBoxNet:
-  with_extra_blocks: true
-  lite_edition: true
-
-LearningRate:
-  base_lr: 0.001
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 300000]
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.0
-    type: RMSPropOptimizer
-  regularizer:
-    factor: 0.0005
-    type: L2
-
-SSDTrainFeed:
-  batch_size: 8
-  use_process: True
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_train_bbx_gt.txt
-    image_dir: WIDER_train/images
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !RandomDistort
-    brightness_lower: 0.875
-    brightness_upper: 1.125
-    is_order: true
-  - !ExpandImage
-    max_ratio: 4
-    prob: 0.5
-  - !CropImageWithDataAchorSampling
-    anchor_sampler:
-    - [1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]
-    batch_sampler:
-    - [1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    - [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]
-    target_size: 640
-  - !RandomInterpImage
-    target_size: 640
-  - !RandomFlipImage
-    is_normalized: true
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDEvalFeed:
-  batch_size: 1
-  use_process: false
-  fields: ['image', 'im_id', 'gt_box']
-  dataset:
-    dataset_dir: dataset/wider_face
-    annotation: wider_face_split/wider_face_val_bbx_gt.txt
-    image_dir: WIDER_val/images
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !NormalizeBox {}
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]
-
-SSDTestFeed:
-  batch_size: 1
-  use_process: false
-  dataset:
-    use_default_label: true
-  drop_last: false
-  image_shape: [3, 640, 640]
-  sample_transforms:
-  - !DecodeImage
-    to_rgb: true
-    with_mixup: false
-  - !ResizeImage
-    interp: 1
-    target_size: 640
-    use_cv2: false
-  - !Permute {}
-  - !NormalizeImage
-    is_scale: false
-    mean: [104, 117, 123]
-    std: [127.502231, 127.502231, 127.502231]

PaddleCV/PaddleDetection/configs/faster_rcnn_r101_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-use_gpu: true
-max_iters: 180000
-log_smooth_window: 20
-save_dir: output
-snapshot_iter: 10000
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r101_1x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  rpn_head: RPNHead
-  roi_extractor: RoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 101
-  feature_maps: 4
-  freeze_at: 2
-
-ResNetC5:
-  depth: 101
-  norm_type: affine_channel
-
-RPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-    use_random: true
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 12000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 6000
-    post_nms_top_n: 1000
-
-RoIAlign:
-  resolution: 14
-  sampling_ratio: 0
-  spatial_scale: 0.0625
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: ResNetC5
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json

PaddleCV/PaddleDetection/configs/faster_rcnn_r101_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
-weights: output/faster_rcnn_r101_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: affine_channel
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r101_fpn_2x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 360000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
-weights: output/faster_rcnn_r101_fpn_2x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: affine_channel
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 320000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r101_vd_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 180000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
-weights: output/faster_rcnn_r101_vd_fpn_1x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: affine_channel
-  variant: d
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r101_vd_fpn_2x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 360000
-snapshot_iter: 10000
-use_gpu: true
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
-weights: output/faster_rcnn_r101_vd_fpn_2x/model_final
-metric: COCO
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  depth: 101
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-  norm_type: affine_channel
-  variant: d
-
-FPN:
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  max_level: 6
-  min_level: 2
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 2000
-    pre_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    post_nms_top_n: 1000
-    pre_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  max_level: 5
-  min_level: 2
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 320000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    image_dir: train2017
-    annotation: annotations/instances_train2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r50_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-use_gpu: true
-max_iters: 180000
-log_smooth_window: 20
-save_dir: output
-snapshot_iter: 10000
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r50_1x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  rpn_head: RPNHead
-  roi_extractor: RoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 50
-  feature_maps: 4
-  freeze_at: 2
-
-ResNetC5:
-  depth: 50
-  norm_type: affine_channel
-
-RPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-    use_random: true
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 12000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 6000
-    post_nms_top_n: 1000
-
-RoIAlign:
-  resolution: 14
-  sampling_ratio: 0
-  spatial_scale: 0.0625
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: ResNetC5
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json

PaddleCV/PaddleDetection/configs/faster_rcnn_r50_2x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-use_gpu: true
-max_iters: 360000
-log_smooth_window: 20
-save_dir: output
-snapshot_iter: 10000
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r50_2x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  rpn_head: RPNHead
-  roi_extractor: RoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 50
-  feature_maps: 4
-  freeze_at: 2
-
-ResNetC5:
-  depth: 50
-  norm_type: affine_channel
-
-RPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-    use_random: true
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 12000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 6000
-    post_nms_top_n: 1000
-
-RoIAlign:
-  resolution: 14
-  sampling_ratio: 0
-  spatial_scale: 0.0625
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: ResNetC5
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [240000, 320000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json

PaddleCV/PaddleDetection/configs/faster_rcnn_r50_fpn_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 90000
-use_gpu: true
-snapshot_iter: 10000
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r50_fpn_1x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: bn
-  norm_decay: 0.
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_lo: 0.0
-  bg_thresh_hi: 0.5
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [60000, 80000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r50_fpn_2x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-max_iters: 180000
-use_gpu: true
-snapshot_iter: 10000
-log_smooth_window: 20
-save_dir: output
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r50_fpn_2x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  fpn: FPN
-  rpn_head: FPNRPNHead
-  roi_extractor: FPNRoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  norm_decay: 0.
-  depth: 50
-  feature_maps: [2, 3, 4, 5]
-  freeze_at: 2
-
-FPN:
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
-
-FPNRPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_start_size: 32
-  min_level: 2
-  max_level: 6
-  num_chan: 256
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_positive_overlap: 0.7
-    rpn_negative_overlap: 0.3
-    rpn_straddle_thresh: 0.0
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 2000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 1000
-    post_nms_top_n: 1000
-
-FPNRoIAlign:
-  canconical_level: 4
-  canonical_size: 224
-  min_level: 2
-  max_level: 5
-  box_resolution: 7
-  sampling_ratio: 2
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_lo: 0.0
-  bg_thresh_hi: 0.5
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: TwoFCHead
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-TwoFCHead:
-  mlp_dim: 1024
-
-LearningRate:
-  base_lr: 0.02
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.3333333333333333
-    steps: 500
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  batch_size: 2
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/coco/annotations/instances_val2017.json
-  batch_transforms:
-  - !PadBatch
-    pad_to_stride: 32
-  drop_last: false
-  num_workers: 2

PaddleCV/PaddleDetection/configs/faster_rcnn_r50_vd_1x.yml

-architecture: FasterRCNN
-train_feed: FasterRCNNTrainFeed
-eval_feed: FasterRCNNEvalFeed
-test_feed: FasterRCNNTestFeed
-use_gpu: true
-max_iters: 180000
-log_smooth_window: 20
-save_dir: output/faster-r50-vd-c4-1x
-snapshot_iter: 10000
-pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
-metric: COCO
-weights: output/faster_rcnn_r50_vd_1x/model_final
-num_classes: 81
-
-FasterRCNN:
-  backbone: ResNet
-  rpn_head: RPNHead
-  roi_extractor: RoIAlign
-  bbox_head: BBoxHead
-  bbox_assigner: BBoxAssigner
-
-ResNet:
-  norm_type: affine_channel
-  depth: 50
-  feature_maps: 4
-  freeze_at: 2
-  variant: d
-
-ResNetC5:
-  depth: 50
-  norm_type: affine_channel
-  variant: d
-
-RPNHead:
-  anchor_generator:
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  rpn_target_assign:
-    rpn_batch_size_per_im: 256
-    rpn_fg_fraction: 0.5
-    rpn_negative_overlap: 0.3
-    rpn_positive_overlap: 0.7
-    rpn_straddle_thresh: 0.0
-    use_random: true
-  train_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 12000
-    post_nms_top_n: 2000
-  test_proposal:
-    min_size: 0.0
-    nms_thresh: 0.7
-    pre_nms_top_n: 6000
-    post_nms_top_n: 1000
-
-RoIAlign:
-  resolution: 14
-  sampling_ratio: 0
-  spatial_scale: 0.0625
-
-BBoxAssigner:
-  batch_size_per_im: 512
-  bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
-  bg_thresh_hi: 0.5
-  bg_thresh_lo: 0.0
-  fg_fraction: 0.25
-  fg_thresh: 0.5
-
-BBoxHead:
-  head: ResNetC5
-  nms:
-    keep_top_k: 100
-    nms_threshold: 0.5
-    score_threshold: 0.05
-
-LearningRate:
-  base_lr: 0.01
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [120000, 160000]
-  - !LinearWarmup
-    start_factor: 0.1
-    steps: 1000
-
-OptimizerBuilder:
-  optimizer:
-    momentum: 0.9
-    type: Momentum
-  regularizer:
-    factor: 0.0001
-    type: L2
-
-FasterRCNNTrainFeed:
-  # batch size per device
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_train2017.json
-    image_dir: train2017
-  drop_last: false
-  num_workers: 2
-
-FasterRCNNEvalFeed:
-  batch_size: 1
-  dataset:
-    dataset_dir: dataset/coco
-    annotation: annotations/instances_val2017.json
-    image_dir: val2017
-  num_workers: 2
-
-FasterRCNNTestFeed:
-  batch_size: 1
-  dataset:
-    annotation: dataset/coco/annotations/instances_val2017.json

PaddleCV/PaddleDetection/contrib/PedestrianDetection/pedestrian.json

-{
-    "images": [],
-    "annotations": [],
-    "categories": [
-        {
-            "supercategory": "component",
-            "id": 1,
-            "name": "pedestrian"
-        }
-   ]
-}

PaddleCV/PaddleDetection/dataset/coco/download_coco.py

-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import sys
-import os.path as osp
-import logging
-
-from ppdet.utils.download import download_dataset
-
-logging.basicConfig(level=logging.INFO)
-
-download_path = osp.split(osp.realpath(sys.argv[0]))[0]
-download_dataset(download_path, 'coco')