Object detection using Tensorflow

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Benefits of this course Able to understand Problem statement. Able to understand how Tensorflow works How Dataset preparation is essential before training the model. How the model is trained and how it may behave similarly to the human brain. (Network of neurons) After training the model, the learner may see it using several graphs and, if it is ready, deploy it in a real-world setting. Course Syllabus Introduction to computer Vision Computer Vision Domain Applications Existing real-world problems AR-VR concepts Tensorflow Tensors Shape Type Graph Anaconda/Miniconda Installation Tensorflow Basic operations Tensorflow CPU Installation Tensorflow GPU Installation (Optional) Module 1: Dataset Preparation Dataset preparation techniques Usage of kaggle platform Understanding the dataset and patterns Preparing standard dataset. Module 2: Dataset Annotation What is annotation/labelling? Why it is required? Supervised model Labelling your own dataset using tools. Module 3: Faster RCNN Algorithm Introduction Coco model Architecture How it works? Module 4: Dataset Conversions Binary Format What is tensorflow record? Why to convert dataset? Conversion of your own prepared dataset. Module 5: Training the model Train the model with tensorflow understanding total loss, learning steps and official commands. Module 6: Visualization on Tensorboard Understanding of how model is trained. Accuracy Generate report Module 7: Deploy your own model to test Test python script Python flask web server Test on webcam Deploy in android Deploy in video feed analysis Pre-requisite: A working laptop or computer Zeal to learn

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