TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10 | TensorFlow Object Detection Classifier for multiple object | Machine Learning library

Anaconda is a software toolkit that creates virtual Python environments so you can install and use Python libraries without worrying about creating version conflicts with existing installations. Anaconda works well on Windows, and enables you to use many Python libraries that normally would only work on a Linux system. It provides a simple method for installing TensorFlow (which we'll do in Step 2d). It also automatically installs the CUDA and cuDNN versions you need for using TensorFlow on a GPU. Download Anaconda for Windows from their webpage (you have to scroll down a ways to get to the download links). Once it's downloaded, execute the installer file and work through the installation steps. If you are using a version of TensorFlow older than TF v1.13, make sure you use the CUDA and cuDNN versions that are compatible with the TensorFlow version you are using. Here is a table showing which version of TensorFlow requires which versions of CUDA and cuDNN. Anaconda will automatically install the correct version of CUDA and cuDNN for the version of TensorFlow you are using, so you shouldn't have to worry about this.
The TensorFlow Object Detection API requires using the specific directory structure provided in its GitHub repository. It also requires several additional Python packages, specific additions to the PATH and PYTHONPATH variables, and a few extra setup commands to get everything set up to run or train an object detection model. This portion of the tutorial goes over the full set up required. It is fairly meticulous, but follow the instructions closely, because improper setup can cause unwieldy errors down the road. Create a folder directly in C: and name it “tensorflow1”. This working directory will contain the full TensorFlow object detection framework, as well as your training images, training data, trained classifier, configuration files, and everything else needed for the object detection classifier. Download the full TensorFlow object detection repository located at https://github.com/tensorflow/models by clicking the “Clone or Download” button and downloading the zip file. Open the downloaded zip file and extract the “models-master” folder directly into the C:\tensorflow1 directory you just created. Rename “models-master” to just “models”. If you are using an older version of TensorFlow, here is a table showing which GitHub commit of the repository you should use. I generated this by going to the release branches for the models repository and getting the commit before the last commit for the branch. (They remove the research folder as the last commit before they create the official version release.). This tutorial was originally done using TensorFlow v1.5 and this GitHub commit of the TensorFlow Object Detection API. If portions of this tutorial do not work, it may be necessary to install TensorFlow v1.5 and use this exact commit rather than the most up-to-date version. TensorFlow provides several object detection models (pre-trained classifiers with specific neural network architectures) in its model zoo. Some models (such as the SSD-MobileNet model) have an architecture that allows for faster detection but with less accuracy, while some models (such as the Faster-RCNN model) give slower detection but with more accuracy. I initially started with the SSD-MobileNet-V1 model, but it didn’t do a very good job identifying the cards in my images. I re-trained my detector on the Faster-RCNN-Inception-V2 model, and the detection worked considerably better, but with a noticeably slower speed. You can choose which model to train your objection detection classifier on. If you are planning on using the object detector on a device with low computational power (such as a smart phone or Raspberry Pi), use the SDD-MobileNet model. If you will be running your detector on a decently powered laptop or desktop PC, use one of the RCNN models. This tutorial will use the Faster-RCNN-Inception-V2 model. Download the model here. Open the downloaded faster_rcnn_inception_v2_coco_2018_01_28.tar.gz file with a file archiver such as WinZip or 7-Zip and extract the faster_rcnn_inception_v2_coco_2018_01_28 folder to the C:\tensorflow1\models\research\object_detection folder. (Note: The model date and version will likely change in the future, but it should still work with this tutorial.). Download the full repository located on this page (scroll to the top and click Clone or Download) and extract all the contents directly into the C:\tensorflow1\models\research\object_detection directory. (You can overwrite the existing "README.md" file.) This establishes a specific directory structure that will be used for the rest of the tutorial.
All files in \object_detection\images\train and \object_detection\images\test
The “test_labels.csv” and “train_labels.csv” files in \object_detection\images
All files in \object_detection\training
All files in \object_detection\inference_graph