更换文档检测模型
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简体中文 | [English](README_en.md)
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# Semi-Supervised Detection (Semi DET) 半监督检测
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## 内容
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- [简介](#简介)
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- [模型库](#模型库)
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- [Baseline](#Baseline)
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- [DenseTeacher](#DenseTeacher)
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- [ARSL](#ARSL)
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- [半监督数据集准备](#半监督数据集准备)
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- [半监督检测配置](#半监督检测配置)
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- [训练集配置](#训练集配置)
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- [预训练配置](#预训练配置)
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- [全局配置](#全局配置)
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- [模型配置](#模型配置)
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- [数据增强配置](#数据增强配置)
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- [其他配置](#其他配置)
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- [使用说明](#使用说明)
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- [训练](#训练)
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- [评估](#评估)
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- [预测](#预测)
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- [部署](#部署)
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- [引用](#引用)
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## 简介
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半监督目标检测(Semi DET)是**同时使用有标注数据和无标注数据**进行训练的目标检测，既可以极大地节省标注成本，也可以充分利用无标注数据进一步提高检测精度。PaddleDetection团队提供了[DenseTeacher](denseteacher/)和[ARSL](arsl/)等最前沿的半监督检测算法，用户可以下载使用。
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## 模型库
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### [Baseline](baseline)
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**纯监督数据**模型的训练和模型库，请参照[Baseline](baseline)；
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### [DenseTeacher](denseteacher)
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| 模型 | 监督数据比例 | Sup Baseline | Sup Epochs (Iters) | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 |
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| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
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| DenseTeacher-FCOS | 5% | [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup005.yml) | 24 (8712) | 21.3 | **30.6** | 240 (87120) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi005.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi005.yml) |
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| DenseTeacher-FCOS | 10% | [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup010.yml) | 24 (17424) | 26.3 | **35.1** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml) |
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| DenseTeacher-FCOS(LSJ)| 10% | [sup_config](./baseline/fcos_r50_fpn_2x_coco_sup010.yml) | 24 (17424) | 26.3 | **37.1(LSJ)** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_semi010_lsj.yml) |
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| DenseTeacher-FCOS |100%(full)| [sup_config](./../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.ymll) | 24 (175896) | 42.6 | **44.2** | 24 (175896)| [download](https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_full.pdparams) | [config](denseteacher/denseteacher_fcos_r50_fpn_coco_full.yml) |
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| 模型 | 监督数据比例 | Sup Baseline | Sup Epochs (Iters) | Sup mAP<sup>val<br>0.5:0.95 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 |
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| :------------: | :---------: | :---------------------: | :---------------------: |:---------------------------: |:----------------------------: | :------------------: |:--------: |:----------: |
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| DenseTeacher-PPYOLOE+_s | 5% | [sup_config](./baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 32.8 | **34.0** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi005.pdparams) | [config](denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi005.yml) |
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| DenseTeacher-PPYOLOE+_s | 10% | [sup_config](./baseline/ppyoloe_plus_crn_s_80e_coco_sup010.yml) | 80 (14480) | 35.3 | **37.5** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_s_coco_semi010.pdparams) | [config](denseteacher/denseteacher_ppyoloe_plus_crn_s_coco_semi010.yml) |
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| DenseTeacher-PPYOLOE+_l | 5% | [sup_config](./baseline/ppyoloe_plus_crn_s_80e_coco_sup005.yml) | 80 (14480) | 42.9 | **45.4** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi005.pdparams) | [config](denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi005.yml) |
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| DenseTeacher-PPYOLOE+_l | 10% | [sup_config](./baseline/ppyoloe_plus_crn_l_80e_coco_sup010.yml) | 80 (14480) | 45.7 | **47.4** | 200 (36200) | [download](https://paddledet.bj.bcebos.com/models/denseteacher_ppyoloe_plus_crn_l_coco_semi010.pdparams) | [config](denseteacher/denseteacher_ppyoloe_plus_crn_l_coco_semi010.yml) |
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### [ARSL](arsl)
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| 模型 | COCO监督数据比例 | Semi mAP<sup>val<br>0.5:0.95 | Semi Epochs (Iters) | 模型下载 | 配置文件 |
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| :------------: | :---------:|:----------------------------: | :------------------: |:--------: |:----------: |
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| ARSL-FCOS | 1% | **22.8** | 240 (87120) | [download](https://paddledet.bj.bcebos.com/models/arsl_fcos_r50_fpn_coco_semi001.pdparams) | [config](arsl/arsl_fcos_r50_fpn_coco_semi001.yml) |
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| ARSL-FCOS | 5% | **33.1** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/arsl_fcos_r50_fpn_coco_semi005.pdparams) | [config](arsl/arsl_fcos_r50_fpn_coco_semi005.yml ) |
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| ARSL-FCOS | 10% | **36.9** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/arsl_fcos_r50_fpn_coco_semi010.pdparams) | [config](arsl/arsl_fcos_r50_fpn_coco_semi010.yml ) |
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| ARSL-FCOS | 10% | **38.5(LSJ)** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/arsl_fcos_r50_fpn_coco_semi010_lsj.pdparams) | [config](arsl/arsl_fcos_r50_fpn_coco_semi010_lsj.yml ) |
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| ARSL-FCOS | full(100%) | **45.1** | 240 (174240) | [download](https://paddledet.bj.bcebos.com/models/arsl_fcos_r50_fpn_coco_full.pdparams) | [config](arsl/arsl_fcos_r50_fpn_coco_full.yml ) |
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## 半监督数据集准备
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半监督目标检测**同时需要有标注数据和无标注数据**，且无标注数据量一般**远多于有标注数据量**。
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对于COCO数据集一般有两种常规设置：
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（1）抽取部分比例的原始训练集`train2017`作为标注数据和无标注数据；
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从`train2017`中按固定百分比（1%、2%、5%、10%等）抽取，由于抽取方法会对半监督训练的结果影响较大，所以采用五折交叉验证来评估。运行数据集划分制作的脚本如下：
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```bash
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python tools/gen_semi_coco.py
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```
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会按照 1%、2%、5%、10% 的监督数据比例来划分`train2017`全集，为了交叉验证每一种划分会随机重复5次，生成的半监督标注文件如下：
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- 标注数据集标注：`instances_train2017.{fold}@{percent}.json`
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- 无标注数据集标注：`instances_train2017.{fold}@{percent}-unlabeled.json`
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其中，`fold` 表示交叉验证，`percent` 表示有标注数据的百分比。
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注意如果根据`txt_file`生成，需要下载`COCO_supervision.txt`:
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```shell
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wget https://bj.bcebos.com/v1/paddledet/data/coco/COCO_supervision.txt
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```
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（2）使用全量原始训练集`train2017`作为有标注数据 和 全量原始无标签图片集`unlabeled2017`作为无标注数据；
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### 下载链接
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PaddleDetection团队提供了COCO数据集全部的标注文件，请下载并解压存放至对应目录:
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```shell
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# 下载COCO全量数据集图片和标注
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# 包括 train2017, val2017, annotations
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wget https://bj.bcebos.com/v1/paddledet/data/coco.tar
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# 下载PaddleDetection团队整理的COCO部分比例数据的标注文件
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wget https://bj.bcebos.com/v1/paddledet/data/coco/semi_annotations.zip
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# unlabeled2017是可选，如果不需要训‘full’则无需下载
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# 下载COCO全量 unlabeled 无标注数据集
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wget https://bj.bcebos.com/v1/paddledet/data/coco/unlabeled2017.zip
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wget https://bj.bcebos.com/v1/paddledet/data/coco/image_info_unlabeled2017.zip
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# 下载转换完的 unlabeled2017 无标注json文件
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wget https://bj.bcebos.com/v1/paddledet/data/coco/instances_unlabeled2017.zip
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```
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如果需要用到COCO全量unlabeled无标注数据集，需要将原版的`image_info_unlabeled2017.json`进行格式转换，运行以下代码:
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<details>
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<summary> COCO unlabeled 标注转换代码：</summary>
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```python
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import json
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anns_train = json.load(open('annotations/instances_train2017.json', 'r'))
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anns_unlabeled = json.load(open('annotations/image_info_unlabeled2017.json', 'r'))
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unlabeled_json = {
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'images': anns_unlabeled['images'],
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'annotations': [],
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'categories': anns_train['categories'],
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}
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path = 'annotations/instances_unlabeled2017.json'
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with open(path, 'w') as f:
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json.dump(unlabeled_json, f)
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```
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</details>
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<details open>
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<summary> 解压后的数据集目录如下：</summary>
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```
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PaddleDetection
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├── dataset
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│ ├── coco
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│ │ ├── annotations
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│ │ │ ├── instances_train2017.json
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│ │ │ ├── instances_unlabeled2017.json
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│ │ │ ├── instances_val2017.json
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│ │ ├── semi_annotations
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│ │ │ ├── instances_train2017.1@1.json
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│ │ │ ├── instances_train2017.1@1-unlabeled.json
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│ │ │ ├── instances_train2017.1@2.json
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│ │ │ ├── instances_train2017.1@2-unlabeled.json
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│ │ │ ├── instances_train2017.1@5.json
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│ │ │ ├── instances_train2017.1@5-unlabeled.json
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│ │ │ ├── instances_train2017.1@10.json
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│ │ │ ├── instances_train2017.1@10-unlabeled.json
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│ │ ├── train2017
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│ │ ├── unlabeled2017
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│ │ ├── val2017
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```
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</details>
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## 半监督检测配置
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配置半监督检测，需要基于选用的**基础检测器**的配置文件，如：
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```python
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_BASE_: [
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'../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
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'../_base_/coco_detection_percent_10.yml',
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]
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log_iter: 50
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snapshot_epoch: 5
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epochs: &epochs 240
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weights: output/denseteacher_fcos_r50_fpn_coco_semi010/model_final
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```
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并依次做出如下几点改动：
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### 训练集配置
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首先可以直接引用已经配置好的半监督训练集，如：
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```python
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_BASE_: [
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'../_base_/coco_detection_percent_10.yml',
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]
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```
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具体来看，构建半监督数据集，需要同时配置监督数据集`TrainDataset`和无监督数据集`UnsupTrainDataset`的路径，**注意必须选用`SemiCOCODataSet`类而不是`COCODataSet`类**，如以下所示:
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**COCO-train2017部分比例数据集**：
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```python
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# partial labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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TrainDataset:
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!SemiCOCODataSet
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image_dir: train2017
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anno_path: semi_annotations/instances_train2017.1@10.json
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dataset_dir: dataset/coco
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data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
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# partial unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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UnsupTrainDataset:
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!SemiCOCODataSet
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image_dir: train2017
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anno_path: semi_annotations/instances_train2017.1@10-unlabeled.json
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dataset_dir: dataset/coco
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data_fields: ['image']
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supervised: False
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```
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或者 **COCO-train2017 full全量数据集**：
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```python
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# full labeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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TrainDataset:
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!SemiCOCODataSet
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image_dir: train2017
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anno_path: annotations/instances_train2017.json
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dataset_dir: dataset/coco
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data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
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# full unlabeled COCO, use `SemiCOCODataSet` rather than `COCODataSet`
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UnsupTrainDataset:
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!SemiCOCODataSet
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image_dir: unlabeled2017
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anno_path: annotations/instances_unlabeled2017.json
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dataset_dir: dataset/coco
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data_fields: ['image']
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supervised: False
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```
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验证集`EvalDataset`和测试集`TestDataset`的配置**不需要更改**，且还是采用`COCODataSet`类。
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### 预训练配置
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```python
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### pretrain and warmup config, choose one and comment another
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
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semi_start_iters: 5000
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ema_start_iters: 3000
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use_warmup: &use_warmup True
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```
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**注意:**
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- `Dense Teacher`原文使用`R50-va-caffe`预训练，PaddleDetection中默认使用`R50-vb`预训练，如果使用`R50-vd`结合[SSLD](../../../docs/feature_models/SSLD_PRETRAINED_MODEL.md)的预训练模型，可进一步显著提升检测精度，同时backbone部分配置也需要做出相应更改，如：
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```python
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
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ResNet:
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [1, 2, 3]
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num_stages: 4
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lr_mult_list: [0.05, 0.05, 0.1, 0.15]
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```
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### 全局配置
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需要在配置文件中添加如下全局配置，并且注意 DenseTeacher 模型需要使用`use_simple_ema: True`而不是`use_ema: True`：
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```python
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### global config
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use_simple_ema: True
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ema_decay: 0.9996
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ssod_method: DenseTeacher
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DenseTeacher:
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train_cfg:
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sup_weight: 1.0
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unsup_weight: 1.0
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loss_weight: {distill_loss_cls: 4.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
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concat_sup_data: True
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suppress: linear
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ratio: 0.01
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gamma: 2.0
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test_cfg:
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inference_on: teacher
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```
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### 模型配置
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如果没有特殊改动，则直接继承自基础检测器里的模型配置。
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以 `DenseTeacher` 为例，选择 `fcos_r50_fpn_iou_multiscale_2x_coco.yml` 作为**基础检测器**进行半监督训练，**teacher网络的结构和student网络的结构均为基础检测器的结构，且结构相同**。
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```python
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_BASE_: [
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'../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
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]
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```
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### 数据增强配置
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构建半监督训练集的Reader，需要在原先`TrainReader`的基础上，新增加`weak_aug`,`strong_aug`,`sup_batch_transforms`和`unsup_batch_transforms`，并且需要注意：
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- 如果有`NormalizeImage`，需要单独从`sample_transforms`中抽出来放在`weak_aug`和`strong_aug`中；
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- `sample_transforms`为**公用的基础数据增强**；
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- 完整的弱数据增强为`sample_transforms + weak_aug`，完整的强数据增强为`sample_transforms + strong_aug`；
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如以下所示:
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原纯监督模型的`TrainReader`：
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```python
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TrainReader:
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sample_transforms:
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- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
|
||||
- RandomFlip: {}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
|
||||
batch_transforms:
|
||||
- Permute: {}
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
- Gt2FCOSTarget:
|
||||
object_sizes_boundary: [64, 128, 256, 512]
|
||||
center_sampling_radius: 1.5
|
||||
downsample_ratios: [8, 16, 32, 64, 128]
|
||||
norm_reg_targets: True
|
||||
batch_size: 2
|
||||
shuffle: True
|
||||
drop_last: True
|
||||
```
|
||||
|
||||
更改后的半监督TrainReader：
|
||||
|
||||
```python
|
||||
### reader config
|
||||
SemiTrainReader:
|
||||
sample_transforms:
|
||||
- Decode: {}
|
||||
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
|
||||
- RandomFlip: {}
|
||||
weak_aug:
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
|
||||
strong_aug:
|
||||
- StrongAugImage: {transforms: [
|
||||
RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
|
||||
RandomErasingCrop: {},
|
||||
RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
|
||||
RandomGrayscale: {prob: 0.2},
|
||||
]}
|
||||
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
|
||||
sup_batch_transforms:
|
||||
- Permute: {}
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
- Gt2FCOSTarget:
|
||||
object_sizes_boundary: [64, 128, 256, 512]
|
||||
center_sampling_radius: 1.5
|
||||
downsample_ratios: [8, 16, 32, 64, 128]
|
||||
norm_reg_targets: True
|
||||
unsup_batch_transforms:
|
||||
- Permute: {}
|
||||
- PadBatch: {pad_to_stride: 32}
|
||||
sup_batch_size: 2
|
||||
unsup_batch_size: 2
|
||||
shuffle: True
|
||||
drop_last: True
|
||||
```
|
||||
|
||||
### 其他配置
|
||||
|
||||
训练epoch数需要和全量数据训练时换算总iter数保持一致，如全量训练24 epoch(换算约为180k个iter)，则10%监督数据的半监督训练，总epoch数需要为240 epoch左右(换算约为180k个iter)。示例如下：
|
||||
|
||||
```python
|
||||
### other config
|
||||
epoch: 240
|
||||
LearningRate:
|
||||
base_lr: 0.01
|
||||
schedulers:
|
||||
- !PiecewiseDecay
|
||||
gamma: 0.1
|
||||
milestones: 240
|
||||
use_warmup: True
|
||||
- !LinearWarmup
|
||||
start_factor: 0.001
|
||||
steps: 1000
|
||||
|
||||
OptimizerBuilder:
|
||||
optimizer:
|
||||
momentum: 0.9
|
||||
type: Momentum
|
||||
regularizer:
|
||||
factor: 0.0001
|
||||
type: L2
|
||||
clip_grad_by_value: 1.0
|
||||
```
|
||||
|
||||
|
||||
## 使用说明
|
||||
|
||||
仅训练时必须使用半监督检测的配置文件去训练，评估、预测、部署也可以按基础检测器的配置文件去执行。
|
||||
|
||||
### 训练
|
||||
|
||||
```bash
|
||||
# 单卡训练 (不推荐，需按线性比例相应地调整学习率)
|
||||
CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
|
||||
|
||||
# 多卡训练
|
||||
python -m paddle.distributed.launch --log_dir=denseteacher_fcos_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml --eval
|
||||
```
|
||||
|
||||
### 评估
|
||||
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams
|
||||
```
|
||||
|
||||
### 预测
|
||||
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=output/denseteacher_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg
|
||||
```
|
||||
|
||||
### 部署
|
||||
|
||||
部署可以使用半监督检测配置文件，也可以使用基础检测器的配置文件去部署和使用。
|
||||
|
||||
```bash
|
||||
# 导出模型
|
||||
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/semi_det/denseteacher/denseteacher_fcos_r50_fpn_coco_semi010.yml -o weights=https://paddledet.bj.bcebos.com/models/denseteacher_fcos_r50_fpn_coco_semi010.pdparams
|
||||
|
||||
# 导出权重预测
|
||||
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU
|
||||
|
||||
# 部署测速
|
||||
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/denseteacher_fcos_r50_fpn_coco_semi010 --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
|
||||
|
||||
# 导出ONNX
|
||||
paddle2onnx --model_dir output_inference/denseteacher_fcos_r50_fpn_coco_semi010/ --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file denseteacher_fcos_r50_fpn_coco_semi010.onnx
|
||||
```
|
||||
|
||||
|
||||
## 引用
|
||||
|
||||
```
|
||||
@article{denseteacher2022,
|
||||
title={Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection},
|
||||
author={Hongyu Zhou, Zheng Ge, Songtao Liu, Weixin Mao, Zeming Li, Haiyan Yu, Jian Sun},
|
||||
journal={arXiv preprint arXiv:2207.02541},
|
||||
year={2022}
|
||||
}
|
||||
```
|
||||
Reference in New Issue
Block a user