目标检测性能指标的计算 test_net.py voc_eval.py详解

最新推荐文章于 2025-03-15 15:37:41 发布

object detection

最新推荐文章于 2025-03-15 15:37:41 发布

阅读量1.6k

点赞数 1

分类专栏：项目一文章标签： Faster rcnn MAP 性能度量目标检测

本文链接：https://blog.youkuaiyun.com/qq_35046132/article/details/90667211

版权

本文详细介绍了目标检测中常用的性能指标计算过程，通过test_net.py和voc_eval.py两个文件的分析，揭示了如何进行Faster R-CNN的测试与性能度量，特别是MAP（平均精度）的计算。读者需了解相关代码实现，参考链接提供了更多细节。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

文件顺序test_demo.py-->test.py-->pascal_voc.py-->voc_eval.py

test_net.py

调用test.py中test_net函数，其中test.py文件在utils文件中

#!/usr/bin/env python

# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen, based on code from Ross Girshick
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os

import tensorflow as tf
from lib.nets.vgg16 import vgg16
from lib.datasets.factory import get_imdb
from lib.utils.test import test_net

# NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_40000.ckpt',)}
DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}


def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN test')
    parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
                        choices=NETS.keys(), default='vgg16')
    parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',
                        choices=DATASETS.keys(), default='pascal_voc_0712')
    args = parser.parse_args()

    return args


if __name__ == '__main__':
    args = parse_args()

    # model path
    demonet = args.demo_net
    dataset = args.dataset
    tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])
    # 获得模型文件名称
    filename = (os.path.splitext(tfmodel)[0]).split('\\')[-1]
    filename = 'default'+'/' + filename
    imdb = get_imdb("voc_2007_test")  # 得到
    imdb.competition_mode('competition mode')
    if not os.path.isfile(tfmodel + '.meta'):
        print(tfmodel)

最低0.47元/天解锁文章