Mainbody Detection

The mainbody detection technology is currently a widely used detection technology, which refers to a whole image recognition process of identifying the coordinate position of one or more objects and then cropping down the corresponding area for recognition. Mainbody detection is the first step of the recognition task, which can effectively improve the recognition accuracy.

This tutorial will introduce the technology from three aspects, namely, the datasets, model selection and model training.

Catalogue

• 1. Dataset
• 2. Model Selection
  • 2.1 Lightweight Mainbody Detection Model
  • 2.2 Server-side Mainbody Detection Model
• 3. Model Training
  • 3.1 Prepare For the Environment
  • 3.2 Prepare For the Dataset
  • 3.3 Configuration Files
  • 3.4 Begin the Training Process
  • 3.5 Model Prediction
  • 3.6 Model Export and Inference Deployment

1. Dataset

The datasets we used for mainbody detection tasks are shown in the following table.

In the actual training process, all datasets are mixed together. Categories of all the labeled boxes are modified as foreground, and the detection model we trained only contains one category (foreground).

2. Model Selection

There are a wide variety of object detection methods, such as the commonly used two-stage detectors (FasterRCNN series, etc.), single-stage detectors (YOLO, SSD, etc.), anchor-free detectors (FCOS, etc.) and so on. PaddleDetection has its self-developed PP-YOLO models for server-side scenarios and PicoDet models for end-side scenarios (CPU and mobile), which all take the lead in the area.

Build on the studies above, PaddleClas provides lightweight and server-side main body detection models for end-side scenarios and server-side scenarios respectively. The table below presents the average mAP of the 5 datasets and the comparison of their model sizes and inference speed.

Notes:

• Detailed information of the CPU of the speed evaluation machine：Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz.The speed indicator is the testing result when mkldnn is on and the number of threads is set to 10.
• Mainbody detection has a time-consuming preprocessing procedure, with an average time of about 40 to 55 ms per image in the above machine. Therefore, it is not included in the inference time.

2.1 Lightweight Mainbody Detection Model

PicoDet, introduced by PaddleDetection, is an object detection algorithm applied to CPU or mobile-side scenarios. It integrates the following optimization algorithm.

• ATSS
• Generalized Focal Loss
• Cosine learning rate decay
• Cycle-EMA
• Lightweight detection head

For more details of optimized PicoDet and benchmark, you can refer to Tutorials of PicoDet Models.

To balance the detection speed and effects in lightweight mainbody detection tasks, we adopt PPLCNet_x2_5 as the backbone of the model and revise the image scale for training and inference to 640x640, with the rest configured the same as picodet_m_shufflenetv2_416_coco.yml. The final detection model is obtained after the training of customized mainbody detection datasets.

2.2 Server-side Mainbody Detection Model

PP-YOLO is proposed by PaddleDetection. It greatly optimizes the yolov3 model from multiple perspectives such as backbone, data augmentation, regularization strategy, loss function, and post-processing. It reaches the state of the art in terms of "speed-precision". The optimization strategy is as follows.

• Better backbone: ResNet50vd-DCN
• Larger training batch size of 8 GPUs and mini-batch size of 24 on each GPU, which is corresponding to learning rate and the number of iterations.
• Drop Block
• Exponential Moving Average
• IoU Loss
• Grid Sensitive
• Matrix NMS
• CoordConv
• Spatial Pyramid Pooling
• Better Pre-trained Model

For more information about PP-YOLO, you can refer to PP-YOLO tutorial.

In the mainbody detection task, we use ResNet50vd-DCN as our backbone for better performance. The config file is ppyolov2_r50vd_dcn_365e_coco.yml, in which the dataset path is modified to the customized mainbody detection dataset. The final detection model can be downloaded here.

3. Model Training

This section mainly talks about how to train your own mainbody detection model using PaddleDetection on your own datasets.

3.1 Prepare For the Environment

Download PaddleDetection and install requirements.

cd <path/to/clone/PaddleDetection>
git clone https://github.com/PaddlePaddle/PaddleDetection.git

cd PaddleDetection
# install requirements
pip install -r requirements.txt

For more installation tutorials, please refer to Installation Tutorial

3.2 Prepare For the Dataset

For customized dataset, you should convert it to COCO format. Please refer to Customized Dataset Tutorial to build your own datasets with COCO format.

In mainbody detection task, all the objects belong to foregroud. Therefore, category_id of all the objects in the annotation file should be modified to 1. And the categories map should be modified as follows, in which just class foregroud is included.

[{u'id': 1, u'name': u'foreground', u'supercategory': u'foreground'}]

3.3 Configuration Files

We use configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml to train the model, mode details are as follows.

ppyolov2_r50vd_dcn_365e_coco.yml depends on other configuration files, their meanings are as follows.

coco_detection.yml：path of train/eval/test dataset.

runtime.yml：public runtime parameters, including whethre to use GPU, epoch number for checkpoint saving, etc.

optimizer_365e.yml：learning rate and optimizer.

ppyolov2_r50vd_dcn.yml：model architecture and backbone.

ppyolov2_reader.yml：train/eval/test reader, such as batch size, the number of concurrently loaded sub-processes, etc., and includes post-read pre-processing operations, such as resize, data enhancement, etc.

In mainbody detection task, you need to modify num_classes in datasets/coco_detection.yml to 1 (only foreground is included), while modify the paths of the training and testing datasets to those of the customized datasets.

In addition, the above files can also be modified according to real situations, for example, if the video memory is overflowing, the batch size and learning rate can be reduced in equal proportion.

3.4 Begin the Training Process

PaddleDetection supports many ways of training process.

• Training using single GPU

# not needed for windows and Mac
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml

• Training using multiple GPUs

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval

--eval: evaluation while training

• (Recommend) Model finetune If you want to finetune the trained model in PaddleClas on your own datasets, you can run the following command.

export CUDA_VISIBLE_DEVICES=0
# assign pretrain_weights, load the general mainbody-detection pretrained model
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o pretrain_weights=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/ppyolov2_r50vd_dcn_mainbody_v1.0_pretrained.pdparams

• Resume training

  you can use -r to load checkpoints and resume training.

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000

Note: If Out of memory error occurs, you can try to decrease batch_size in ppyolov2_reader.yml while reducing learning rate in equal proportion.

3.5 Model Prediction

Use the following command to finish the prediction process.

export CUDA_VISIBLE_DEVICES=0
python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=your_image_path.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final

--draw_threshold is an optional parameter. According to NMS calculation, different thresholds will produce different results. keep_top_k indicates the maximum number of output targets, with a default value of 100 that can be modified according to their actual situation.

3.6 Model Export and Inference Deployment

Use the following to export the inference model：

python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams

The inference model will be saved under the directory inference/ppyolov2_r50vd_dcn_365e_coco, which containsinfer_cfg.yml (optional for mainbody detection), model.pdiparams, model.pdiparams.info, model.pdmodel.

Note： Inference model that PaddleDetection exports is named model.xxx，if you want to keep it consistent with PaddleClas，you can rename model.xxx to inference.xxx for subsequent inference deployment of mainbody detection.

For more model export tutorials, please refer to EXPORT_MODEL.

The final directory contains inference/ppyolov2_r50vd_dcn_365e_coco, inference.pdiparams, inference.pdiparams.info, and inference.pdmodel，among whichinference.pdiparams refers to saved weight files of the inference model while inference.pdmodel stands for structural files.

After exporting the model, the path of the detection model can be changed to the inference model path to complete the prediction task.

Take product recognition as an example，you can modify the field Global.det_inference_model_dir in its config file inference_product.yaml to the directory of exported inference model, and then finish the detection and recognition of the product with reference to Quick Start for Image Recognition.

FAQ

Q: Is it compatible with other mainbody detection models？

• A: Yes, but the current preprocessing process only supports PicoDet and YOLO models, so it is recommended to use these two for training. If you want to use other models such as Faster RCNN, you need to revise the logic of preprocessing in accordance with that of PaddleDetection. You are welcomed to resort to Github Issue or WeChat group for any needs or questions.

Q: Can I modify the prediction scale of mainbody detection?

• A: Yes, but there are 2 things that require attention
  • The mainbody detection model provided in PaddleClas is trained based on 640x640 resolution, so this is also the default value of prediction process. The accuracy will be reduced if other resolutions are used.
  • When exporting the model, it is recommended to modify the resolution of the exported model to keep it consistent with the prediction process.