Implementation of TensorFlow Object Detection API on Windows 10 with fruit images without Anaconda Distribution.

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No Anaconda Distribution. Use command prompt instead of Anaconda Prompt.

1)Don't use the below commands in 2d.

conda create -n tensorflow1 pip python=3.5

activate tensorflow1

2)Change the command below in 2d

conda install -c anaconda protobuf

to this

pip install protobuf --upgrade

3)Download protoc.exe from here . Copy protoc.exe(in bin folder) to C:tensorflow1modelsresearch.

Then run 2f.

4)Before step6, open C:tensorflow1modelsresearchobject_detectionutilslearning_schedules.py with a text editor.

Change the line 168 from

range(num_boundaries)

to

list(range(num_boundaries))

Data can be downloaded from here. 240 training images 60 test images. No problematic image.

3 different types of fruits: Apple, Banana, Orange.

.xml files in data have coordinates of objects.

I trained it on a GTX 1050 for 5 hours. Approximately 53000 iterations.

Faster R CNN inception v2 model.

Credits go to EdgeElectronics. I just followed the steps in the tutorial.

With just a few lines of MATLAB^® code, you can build machine learning and deep learning models for object recognition without having to be an expert.

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Using MATLAB for object recognition enables you to be successful in less time because it lets you:

1. Use your domain expertise and learn data science with MATLAB:

   You can use MATLAB to learn and gain expertise in the areas of machine learning and deep learning. MATLAB makes learning about these fields practical and accessible. In addition, MATLAB enables domain experts to create object recognition models – instead of handing the task over to data scientists who may not know your industry or application.

2. Use apps to label data and build models:

   MATLAB lets you build machine learning and deep learning models with minimal code.

   With the Classification Learner app, you can quickly build machine learning models and compare different machine learning algorithms without writing code.

   Using the Image Labeler app, you can interactively label objects within images and automate ground truth labeling within videos for training and testing deep learning models. This interactive and automated approach can lead to better results in less time.

3. Integrate object recognition in a single workflow:

   MATLAB can unify multiple domains in a single workflow. With MATLAB, you can do your thinking and programming in one environment. It offers tools and functions for deep learning and machine learning, and also for a range of domains that feed into these algorithms, such as robotics, computer vision, and data analytics.

MATLAB automates deploying your models on enterprise systems, clusters, clouds, and embedded devices.