PaddleDetection-PP-YOLOv2 模型

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我是靠谱客的博主强健薯片，最近开发中收集的这篇文章主要介绍PaddleDetection-PP-YOLOv2 模型，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

2021SC@SDUSC本周分析

PP-YOLO模型库

Model	GPU number	images/GPU	backbone	input shape	Box APval	Box APtest	V100 FP32(FPS)	V100 TensorRT FP16(FPS)	download	config
PP-YOLO	8	24	ResNet50vd	608	44.8	45.2	72.9	155.6	model	config
PP-YOLO	8	24	ResNet50vd	512	43.9	44.4	89.9	188.4	model	config
PP-YOLO	8	24	ResNet50vd	416	42.1	42.5	109.1	215.4	model	config
PP-YOLO	8	24	ResNet50vd	320	38.9	39.3	132.2	242.2	model	config
PP-YOLO_2x	8	24	ResNet50vd	608	45.3	45.9	72.9	155.6	model	config
PP-YOLO_2x	8	24	ResNet50vd	512	44.4	45.0	89.9	188.4	model	config
PP-YOLO_2x	8	24	ResNet50vd	416	42.7	43.2	109.1	215.4	model	config
PP-YOLO_2x	8	24	ResNet50vd	320	39.5	40.1	132.2	242.2	model	config
PP-YOLO	4	32	ResNet18vd	512	29.2	29.5	357.1	657.9	model	config
PP-YOLO	4	32	ResNet18vd	416	28.6	28.9	409.8	719.4	model	config
PP-YOLO	4	32	ResNet18vd	320	26.2	26.4	480.7	763.4	model	config
PP-YOLOv2	8	12	ResNet50vd	640	49.1	49.5	68.9	106.5	model	config
PP-YOLOv2	8	12	ResNet101vd	640	49.7	50.3	49.5	87.0	model	config

修改配置文件ppyolov2_r50vd_dcn_365e_coco.yml

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  './_base_/ppyolov2_r50vd_dcn.yml',
  './_base_/optimizer_365e.yml',
  './_base_/ppyolov2_reader.yml',
]

修改配置文件`PaddleDetection/configs/datasets/coco_detection.yml`

metric: COCO
num_classes: 4  # 不包含背景类

TrainDataset:
  !COCODataSet
    image_dir: images
    anno_path: annotations/train.json
    dataset_dir: /home/aistudio/data
    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']

EvalDataset:
  !COCODataSet
    image_dir: images
    anno_path: annotations/valid.json
    dataset_dir: /home/aistudio/data

TestDataset:
  !ImageFolder
    anno_path: /home/aistudio/data/annotations/valid.json

训练迭代了500epoch

使用单GPU通过如下命令一键式评估模型在COCO val2017数据集效果

# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams

# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo.yml -o weights=output/ppyolo/best_model

# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams

# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=output/ppyolo/best_model

# 推理单张图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439_640x640.jpg

# 推理目录下所有图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_dir=demo

# 推理单张图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439_640x640.jpg

# 推理目录下所有图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_dir=demo

# 推理单张图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439_640x640.jpg

# 推理目录下所有图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_dir=demo

# 导出模型，通过--exclude_nms参数裁剪掉模型中的NMS部分，默认存储于output/ppyolo目录
python tools/export_model.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --exclude_nms

# FP32 benchmark测试
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True

# TensorRT FP16 benchmark测试
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True --run_mode=trt_fp16