tensorflow object detection API安装

http://blog.csdn.net/u010122972/article/details/77385793

终端进入models根目录 ==>>  models/research

object_detection 能够对ssd_mobilenets进行训练，为了体验效果，对object_detection 进行了安装

1.安装依赖项

我的是ubuntu 14.04，故在终端中输入如下命令

sudo pip install pillowsudo pip install lxmlsudo pip install jupytersudo pip install matplotlib

如果是ubuntu 16.04，可输入：

sudo apt-get install protobuf-compiler python-pil python-lxmlsudo pip install jupytersudo pip install matplotlib

2.编译protobuf

终端进入models根目录，输入

protoc object_detection/protos/*.proto --python_out=.

（默认已经安装protobuf）

3.添加库路径

终端进入models根目录，输入

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

每次终端都需要输入一次

4.验证

终端进入models根目录，输入

python object_detection/builders/model_builder_test.py

若显示OK，则已经成功安装

===============================update===================================

http://blog.csdn.net/u010302327/article/details/78248394

train自己的pb：

1. python object_detection/train.py --train_dir object_detection/train --pipeline_config_path object_detection/VOC2012/ssd_mobilenet_v1_voc2012.config

报错：

https://github.com/tensorflow/models/issues/1817

2017-06-29 17:24:13.193833: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:7) -> (device: 7, name: Tesla K80, pci bus id: 0000:00:0b.0)2017-06-29 17:24:15.414228: I tensorflow/core/common_runtime/simple_placer.cc:675] Ignoring device specification /device:GPU:0 for node 'prefetch_queue_Dequeue' because the input edge from 'prefetch_queue' is a reference connection and already has a device field set to /device:CPU:0INFO:tensorflow:Restoring parameters from /home/ubuntu/models/data_xxxx/model.ckptINFO:tensorflow:Starting Session.INFO:tensorflow:Saving checkpoint to path data_doliprane/model.ckptINFO:tensorflow:Starting Queues.INFO:tensorflow:global_step/sec: 0[1]    4359 killed     python object_detection/train.py --train_dir=data_xxxx 

1.1 修改如下，错误依旧：

https://stackoverflow.com/questions/45150773/tensorflow-object-detection-training-killed-resource-starvation

To quote from the issue, with my comments:

The section in your new config will look like this:

train_input_reader: { tf_record_input_reader { input_path: "PATH_TO_BE_CONFIGURED/pet_train.record" } label_map_path: "PATH_TO_BE_CONFIGURED/pet_label_map.pbtxt"queue_capacity: 100 # change this number min_after_dequeue: 10 # change this number (strictly less than the above) }

You can also set these for eval_input_reader. For this one I am using 20, 10 and for train I use 100, 10, although I think I could go lower. My training takes less than 8Gb of RAM.

1.2 修改如下，可以跑了：
Hi again guys, we have found a solutionchanging the batch_size to one. By default this parameter is set to 32, so probably this needs too much RAM.
I don't understand why this is consuming this extremely amount of RAM, but you can change this and train a model in a normal environment.

2. thus we have a pb file.....

$ python object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path object_detection/VOC2012/ssd_mobilenet_v1_voc2012.config --trained_checkpoint_prefix object_detection/train/model.ckpt-200 --output_directory object_detection/VOC2012/model/

3. tensorborad

jiao@jiao-linux:~/code/source/tensorflow/models/research$ tensorboard --logdir='home/jiao/code/source/tensorflow/models/reseatch/object_detection/VOC2012/ssd_mobilenet_train_logs'
TensorBoard 0.4.0rc3 at http://jiao-linux:6006 (Press CTRL+C to quit)

4.利用训练好的模型进行图片测试

（1）下载labelimage源码
curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/r1.3/tensorflow/examples/label_image/label_image.py

（2）read it README.md