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Snehit Vaddi's Projects

braintumorclassification icon braintumorclassification

Comparison and Analysis of various DL models based is proposed to classify different brain tumor types using publicly available dataset. This classifies tumors into (meningioma, glioma, and pituitary tumor). The proposed network structure achieves a significant performance with the best overall accuracy of 90%. The results indicate the ability of the model for brain tumor multi-classification purposes.

bytetrack icon bytetrack

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

citrus-ecoli-fluoronet icon citrus-ecoli-fluoronet

An advanced system that classifies different concentrations of E. coli bacteria on citrus fruit surfaces. By analyzing fluorescence images, employing deep learning, and utilizing image wavelet analysis, this tool accurately identifies and categorizes E. coli contamination levels.

deep-fake_first_order_model icon deep-fake_first_order_model

This Repo consists of implementing First order motion model for making Deep Fakes. It is referenced from a video on youtube by Two Minute Papers about Deep Fakes. The code given by @AliaksandrSiarohin

deepfake-using-wave2lip icon deepfake-using-wave2lip

A deep learning model to lip-sync a given video with any given audio. It uses GAN architecture to orchestrate loss reconstruction or training.

earcv icon earcv

An Open-Source, Computer Vision Package for Maize Ear Phenotyping

facemask-detection-using-deeplearning icon facemask-detection-using-deeplearning

A python tool to detect if a person is wearing a mask or not in real-time using deep learning techniques. It works with images, videos, and real-time videos as well.

google-colab-keep-alive icon google-colab-keep-alive

Automatically clicks on the web page every few seconds to keep sessions active. Perfect for platforms that time out due to inactivity. Connect with the developer on LinkedIn and GitHub!

hand-writing-detection_cnn icon hand-writing-detection_cnn

Predicting hand written digits in realtime using keras. Here model training is done using MNIST dataset. I used paint like screen to write digits.

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