For example, I took about 25 photos of each microcontroller and 25 pictures containing multiple microcontrollers for my microcontroller detector. You can either take the pictures yourself or download them from the internet. So they should have different backgrounds, random objects, and varying lighting conditions. To train a robust classifier, we need a lot of pictures that should differ a lot from each other. ModelBuilderTF1Test.test_unknown_ssd_feature_extractorīefore we can start creating the object detector, we need data that we can use for training. ModelBuilderTF1Test.test_unknown_meta_architecture ModelBuilderTF1Test.test_unknown_faster_rcnn_feature_extractor ModelBuilderTF1Test.test_invalid_second_stage_batch_size ModelBuilderTF1Test.test_invalid_model_config_proto ModelBuilderTF1Test.test_invalid_first_stage_nms_iou_threshold ModelBuilderTF1Test.test_invalid_faster_rcnn_batchnorm_update ModelBuilderTF1Test.test_create_ssd_models_from_config ModelBuilderTF1Test.test_create_ssd_fpn_model_from_config ModelBuilderTF1Test.test_create_rfcn_model_from_config ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul ModelBuilderTF1Test.test_create_faster_rcnn_model_from_config_with_example_miner ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature(False) ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature(True) ModelBuilderTF1Test.test_create_experimental_model ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params(False) ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params(True) If everything is installed correctly, you should see something like: Running tests under Python 3.6.9: /usr/bin/python3 Python object_detection/builders/model_builder_tf1_test.py To test the installation, run: # Test the installation. Os.system(protoc_path+" "+directory+"/"+file+" -python_out=.") python use_protobuf.py To make this easier, I created a python script that loops through a directory and converts all proto files one at a time. If you are using version 3.5, you have to go through each file individually. Note: The *.proto designating all files does not work protobuf version 3.5 and higher. # Install TensorFlow Object Detection API.Ĭp object_detection/packages/tf1/setup.py. Protoc object_detection/protos/*.proto -python_out=. ĭocker run -it od Python Package Installation cd models/research If you aren't familiar with Docker though, it might be easier to install it using pip.įirst clone the master branch of the Tensorflow Models repository: git clone Docker Installation # From the root of the git repositoryĭocker build -f research/object_detection/dockerfiles/tf1/Dockerfile -t od. For running the Tensorflow Object Detection API locally, Docker is recommended. You can install the TensorFlow Object Detection API with Python Package Installer (pip) or Docker, an open-source platform for deploying and managing containerized applications. This article will go over all the steps needed to create our object detector, from gathering the data to testing our newly created object detector. Figure 1: Tensorflow Object Detection Tutorial Video Introduction I chose to create an object detector that can distinguish between four different microcontrollers. In this article, we will go through training your own object detector for whichever objects you like. Object detection is the craft of detecting instances of a certain class, like animals, humans, and many more, in an image or video. You can find a Tensorflow 2 version of this article here. Update : The Tensorflow Object Detection API now officially supports Tensorflow 2.
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