Plant-Detection-Using-TensorFlow | Plant identification based on leaf structure | Database library

Install TensorFlow-GPU and CPU by following the instructions or you can follow YouTube Video by Mark Jay. The video is made for TensorFlow-GPU v1.4, but the “pip install --upgrade tensorflow-gpu or pip install --upgrade tensorflow (FOR CPU)” command will automatically download version 1.5. Download and install CUDA v9.0 and cuDNN v7.0 (rather than CUDA v8.0 and cuDNN v6.0 as instructed in the video), because they are supported by TensorFlow-GPU v1.5. As future versions of TensorFlow are released, you will likely need to continue updating the CUDA and cuDNN versions to the latest supported version. Be sure to install Anaconda with Python 3.6 as instructed in the video, as the Anaconda virtual environment will be used for the rest of this tutorial. Visit TensorFlow's website for further installation details, including how to install it on other operating systems (like Linux). The object detection repository itself also has installation instructions.
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”. (Note, this tutorial was done using this GitHub commit of the TensorFlow Object Detection API. If portions of this tutorial do not work, it may be necessary to download and use this exact commit rather than the most up-to-date version.). (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) 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 because my local machine(laptop) configurations is lower and I am training my dataset on CPU (no GPU). If you have the higher configuration laptop with decent NVDIA graphics card then you can make use of Faster-RCNN-Inception-V2 model, and the detection works considerably better, but with a noticeably slower speed. This tutorial will use the ssd_mobilenet_v1_coco model. Download the model here (http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz). Open the downloaded ssd_mobilenet_v1_coco file with a file archiver such as WinZip or 7-Zip and extract the ssd_mobilenet_v1_coco 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.