from object_detection.utils import label_map_util, config_util from object_detection.utils import visualization_utils as viz_utils from object_detection.builders import model_builder PATH_TO_CFG = PATH_TO_MODEL_DIR + '/pipeline.config' PATH_TO_CKPT = PATH_TO_MODEL_DIR + '/checkpoint' print(...
sys.path.append("../..")print(sys.path)#from utils import label_map_util#from utils import visualization_utils as vis_utilfromresearch.object_detection.utilsimportlabel_map_utilfromresearch.object_detection.utilsimportvisualization_utils as vis_util#What model to download.MODEL_NAME ='ssd_mobilene...
打开object_detection\g3doc\tf2_classification_zoo.md,里面是用相应数据集训练好的模型(有附下载链接),可直接在上面做迁移训练。 如果已经下载或有训练好的模型,直接使用model=tf.saved_model.load加载即可(这个函数在tf1中就比较麻烦了) ②模型的输入输出(可略过) #detection_model.signatures['serving_default']...
(还是写一下吧,找到对应python环境。例如前面创建的python是tensorflowAPI ,那就找到anaconda的安装路径,envs文件夹,进入tensorflowAPI\Lib\site-packages,将前面的object_detection文件夹复制进去) 测试环境 python object_detection/builders/model_builder_test.py 1. 出现上图,环境正常 我配置好的环境 下载地址:https...
python object_detection/builders/model_builder_tf2_test.py 1. 之后要用的文件都在research/object_detection 文件夹里 我的选择是把整个object_detection 文件夹复制到pycharm 工程里, 如果出现找不到模块错误,就把整个object_detection 文件夹复制到对应的python环境的site-package里 ...
Tensorflow Object Detection API框架 基于tensorflow框架构建的快速对象检测模型构建、训练、部署框架,是针对计算机视觉领域对象检测任务的深度学习框架。之前tensorflow2.x一直不支持该框架,最近Tensorflow Object Detection API框架最近更新了,同时支持tensorflow1.x与tensorflow2.x。其中model zoo方面,tensorflow1.x基于COCO数...
from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util # What model to download. MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' MODEL_FILE = MODEL_NAME + '.tar.gz'
1.1."train.py"文件在/object_detection/legacy当中,把它放入到/object_detection中, 在路径\object_detection下输入指令: (object_dection) E:\4work\8python\1study\object_detection\object-detection-model\research\object_detection>python train.py --logtostderr --train_dir=training/ --pipeline_config_...
它保存了网络的结构和数据 PATH_TO_CKPT=MODEL_NAME+'/frozen_inference_graph.pb' # mscoco_label_map.pbtxt文件中保存了index到类别名的映射 # 如神经网络的预测类别是5,必须要通过这个文件才能知道index具体对应的类别是什么 # mscoco_label_map.pbtxt文件就保存在object_detection/data文件夹下,读者可以自 ...
1.1."train.py"文件在/object_detection/legacy当中,把它放入到/object_detection中, 在路径\object_detection下输入指令: (object_dection) E:\4work\8python\1study\object_detection\object-detection-model\research\object_detection>python train.py --logtostderr --train_dir=training/ --pipeline_config_...