COVID-CT
Face-Mask-Detection
COVID-CT | Face-Mask-Detection | |
---|---|---|
1 | 1 | |
1,085 | 1,501 | |
- | - | |
0.0 | 0.0 | |
4 months ago | about 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
- | MIT License |
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COVID-CT
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Build an Image Classification API Using Django Rest Framework.
CT Scan images (750 images) were obtained from: https://github.com/UCSD-AI4H/COVID-CT/tree/master/Data-split
Face-Mask-Detection
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Need help !
Yea re-reading my plan I realize data collection was kinda unneeded! Look through this model https://github.com/chandrikadeb7/Face-Mask-Detection it's one of the better ones I've seen. I'm sure there's a competent dev out there who could take this and help you out. Good luck!!
What are some alternatives?
Django-Covid-19-Detector-API
borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.
SKAB - SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.
SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2 - This repository contains the script and process to create custom SSD Mobilenet model for object detection
mac-miller-lyrics-dataset - Dataset with lyrics from Mac Miller
deepfake-detection - DeepFake Detection: Detect the video is fake or not using InceptionResNetV2.
xrays-and-gradcam - Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Mask-RCNN-Implementation - Mask RCNN Implementation on Custom Data(Labelme)
covid-chestxray-dataset - We are building an open database of COVID-19 cases with chest X-ray or CT images.
strv-ml-mask2face - Virtually remove a face mask to see what a person looks like underneath
handwritten-digits-recognizer-webapp - This is my first experience with machine learning
image-crop-analysis - Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency