In this article, you will learn how to train a Blood face detector with Python, OpenCV, Keras/TensorFlow, and Deep Learning
In part-1 of Blood Face Detector, I will show how our_ computer vision/deep learning_ pipeline will be implemented. Then we’ll prepare the dataset used to train our custom blood face detector. Next, we will implement a_ Python script to train a face mask detector _on our dataset using Keras and TensorFlow. Once the training of the model is done we can evaluate the results on unseen images as well as in real-time.
In** part-2** of this article, we will learn how to_ make an end-to-end interactive dashboard/ web application with a streamlit framework in python._
The dataset contains:
Dataset is already prepared for you guys by _**_detecting the face and cropping only the face part_ from the original dataset and can be downloaded from here _[drive](https://drive.google.com/drive/folders/1ESZKixKC2O3SKIoZ71ZWr6ups07t8haS?usp=sharing).**
In case you want to go through the _**_data_preperation python script_ which has been used for the purpose of detecting the faces, cropping the face part and saving the dataset for the purpose of training of model can be accessed here_[Github](https://github.com/abhiwalia15/Face-Classification-into-Blood-No-Blood).**
Note: Our goal in today’s article is to train a custom deep learning model to detect whether a person has blood on his face or not.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
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