faster rcnn 的重新训练

最新推荐文章于 2025-06-21 17:14:05 发布

denyz

最新推荐文章于 2025-06-21 17:14:05 发布

阅读量3.3k

点赞数 1

CC 4.0 BY-SA版权

分类专栏： Deep Learning 文章标签： faster rcnn 训练

本文链接：https://blog.youkuaiyun.com/denyz/article/details/52443032

Deep Learning 专栏收录该内容

2 篇文章

订阅专栏

本文介绍如何安装并使用Faster R-CNN进行目标检测模型的训练与测试，包括下载数据集、预训练模型及配置环境等步骤。

Beyond the demo: installation for training and testing models

Download the training, validation, test data and VOCdevkit

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar

Extract all of these tars into one directory named VOCdevkit

tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar
tar xvf VOCdevkit_08-Jun-2007.tar

It should have this basic structure

$VOCdevkit/                           # development kit
$VOCdevkit/VOCcode/                   # VOC utility code
$VOCdevkit/VOC2007                    # image sets, annotations, etc.
# ... and several other directories ...

Create symlinks for the PASCAL VOC dataset

cd $FRCN_ROOT/data #这里需要注意，$VOCdevkit代表路径变量，若echo $VOCdevkit为空则不能成功   
ln -s $VOCdevkit VOCdevkit2007 #因此要先设置临时路径变量VOCdevkit=../VOCdevkit，因为我VOCdevkit目录在$FRCN_ROOT下

Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.

[Optional] follow similar steps to get PASCAL VOC 2010 and 2012
[Optional] If you want to use COCO, please see some notes under data/README.md
Follow the next sections to download pre-trained ImageNet models

Download pre-trained ImageNet models

Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16.

cd $FRCN_ROOT
./data/scripts/fetch_imagenet_models.sh

VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience. ZF was trained at MSRA.

Usage

To train and test a Faster R-CNN detector using the alternating optimization algorithm from our NIPS 2015 paper, useexperiments/scripts/faster_rcnn_alt_opt.sh. Output is written underneath $FRCN_ROOT/output.

cd $FRCN_ROOT
./experiments/scripts/faster_rcnn_alt_opt.sh [GPU_ID] [NET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use
# --set ... allows you to specify fast_rcnn.config options, e.g.
#   --set EXP_DIR seed_rng1701 RNG_SEED 1701

("alt opt" refers to the alternating optimization training algorithm described in the NIPS paper.)

To train and test a Faster R-CNN detector using the approximate joint training method, useexperiments/scripts/faster_rcnn_end2end.sh. Output is written underneath $FRCN_ROOT/output.

cd $FRCN_ROOT
./experiments/scripts/faster_rcnn_end2end.sh [GPU_ID] [NET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use
# --set ... allows you to specify fast_rcnn.config options, e.g.
#   --set EXP_DIR seed_rng1701 RNG_SEED 1701

This method trains the RPN module jointly with the Fast R-CNN network, rather than alternating between training the two. It results in faster (~ 1.5x speedup) training times and similar detection accuracy. See these slides for more details.

Artifacts generated by the scripts in tools are written in this directory.

Trained Fast R-CNN networks are saved under:

output/<experiment directory>/<dataset name>/

Test outputs are saved under:

output/<experiment directory>/<dataset name>/<network snapshot name>/